Archive Layout with Content
A variety of common markup showing how the theme styles them.
Blockquotes
Single line blockquote:
Quotes are cool.
Tables
| Entry | Item | |
|---|
| John Doe | 2016 | Description of the item in the list |
| Jane Doe | 2019 | Description of the item in the list |
| Doe Doe | 2022 | Description of the item in the list |
| Header1 | Header2 | Header3 |
|---|
| cell1 | cell2 | cell3 |
| cell4 | cell5 | cell6 |
| cell1 | cell2 | cell3 |
| cell4 | cell5 | cell6 |
| Foot1 | Foot2 | Foot3 |
Definition Lists
- Definition List Title
- Definition list division.
- Startup
- A startup company or startup is a company or temporary organization designed to search for a repeatable and scalable business model.
- #dowork
- Coined by Rob Dyrdek and his personal body guard Christopher “Big Black” Boykins, “Do Work” works as a self motivator, to motivating your friends.
- Do It Live
- I’ll let Bill O’Reilly explain this one.
Unordered Lists (Nested)
- List item one
- List item one
- List item one
- List item two
- List item three
- List item four
- List item two
- List item three
- List item four
- List item two
- List item three
- List item four
Ordered List (Nested)
- List item one
- List item one
- List item one
- List item two
- List item three
- List item four
- List item two
- List item three
- List item four
- List item two
- List item three
- List item four
Make any link standout more when applying the .btn class.
Notices
Watch out! You can also add notices by appending {: .notice} to a paragraph.
Address Tag
1 Infinite Loop
Cupertino, CA 95014
United StatesAnchor Tag (aka. Link)
This is an example of a link.
Abbreviation Tag
The abbreviation CSS stands for “Cascading Style Sheets”.
Cite Tag
“Code is poetry.” —Automattic
Code Tag
You will learn later on in these tests that word-wrap: break-word; will be your best friend.
Strike Tag
This tag will let you strikeout text.
Emphasize Tag
The emphasize tag should italicize text.
Insert Tag
This tag should denote inserted text.
Keyboard Tag
This scarcely known tag emulates keyboard text, which is usually styled like the <code> tag.
This tag styles large blocks of code.
.post-title {
margin: 0 0 5px;
font-weight: bold;
font-size: 38px;
line-height: 1.2;
and here's a line of some really, really, really, really long text, just to see how the PRE tag handles it and to find out how it overflows;
}
Quote Tag
Developers, developers, developers…
–Steve Ballmer
Strong Tag
This tag shows bold text.
Subscript Tag
Getting our science styling on with H2O, which should push the “2” down.
Superscript Tag
Still sticking with science and Isaac Newton’s E = MC2, which should lift the 2 up.
Variable Tag
This allows you to denote variables.
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
Takayuki Kanai, Igor Vasiljevic, Vitor Guizilini, Adrien Gaidon, Rares Ambrus
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely. Multi-camera self-supervised monocular depth estimation from videos is a promising way to reason about the environment, as it generates metrically scaled geometric predictions from visual data without requiring additional sensors. However, most works assume well-calibrated extrinsics to fully leverage this multi-camera setup, even though accurate and efficient calibration is still a challenging problem. In this work, we introduce a novel method for extrinsic calibration that builds upon the principles of self-supervised monocular depth and ego-motion learning. Our proposed curriculum learning strategy uses monocular depth and pose estimators with velocity supervision to estimate extrinsics, and then jointly learns extrinsic calibration along with depth and pose for a set of overlapping cameras rigidly attached to a moving vehicle. Experiments on a benchmark multi-camera dataset (DDAD) demonstrate that our method enables self-calibration in various scenes robustly and efficiently compared to a traditional vision-based pose estimation pipeline. Furthermore, we demonstrate the benefits of extrinsics self-calibration as a way to improve depth prediction via joint optimization.
@inproceedings{tri_sesc_iros23, author = {Takayuki Kanai and Igor Vasiljevic and Vitor Guizilini and Adrien Gaidon and Rares Ambrus}, title = {Robust Self-Supervised Extrinsic Self-Calibration}, booktitle = {The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, year = {2023} }
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