High-performance and tunable stereo reconstruction
Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In t...
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| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2017
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| Online Access: | http://hdl.handle.net/1721.1/107456 https://orcid.org/0000-0001-7198-1772 https://orcid.org/0000-0002-8863-6550 |
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| author | Ramalingam, Srikumar Pillai, Sudeep Leonard, John J |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ramalingam, Srikumar Pillai, Sudeep Leonard, John J |
| author_sort | Ramalingam, Srikumar |
| collection | MIT |
| description | Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles. |
| first_indexed | 2024-09-23T10:37:36Z |
| format | Article |
| id | mit-1721.1/107456 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:37:36Z |
| publishDate | 2017 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1074562022-09-27T10:08:53Z High-performance and tunable stereo reconstruction Ramalingam, Srikumar Pillai, Sudeep Leonard, John J Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Department of Mechanical Engineering Pillai, Sudeep Leonard, John J Traditional stereo algorithms have focused their efforts on reconstruction quality and have largely avoided prioritizing for run time performance. Robots, on the other hand, require quick maneuverability and effective computation to observe its immediate environment and perform tasks within it. In this work, we propose a high-performance and tunable stereo disparity estimation method, with a peak frame-rate of 120Hz (VGA resolution, on a single CPU-thread), that can potentially enable robots to quickly reconstruct their immediate surroundings and maneuver at high-speeds. Our key contribution is a disparity estimation algorithm that iteratively approximates the scene depth via a piece-wise planar mesh from stereo imagery, with a fast depth validation step for semi-dense reconstruction. The mesh is initially seeded with sparsely matched keypoints, and is recursively tessellated and refined as needed (via a resampling stage), to provide the desired stereo disparity accuracy. The inherent simplicity and speed of our approach, with the ability to tune it to a desired reconstruction quality and runtime performance makes it a compelling solution for applications in high-speed vehicles. 2017-03-17T13:38:43Z 2017-03-17T13:38:43Z 2016-06 2016-05 Article http://purl.org/eprint/type/ConferencePaper 978-1-4673-8026-3 http://hdl.handle.net/1721.1/107456 Pillai, Sudeep, Srikumar Ramalingam, and John J. Leonard. “High-Performance and Tunable Stereo Reconstruction.” IEEE, 2016. 3188–3195. https://orcid.org/0000-0001-7198-1772 https://orcid.org/0000-0002-8863-6550 en_US http://dx.doi.org/10.1109/ICRA.2016.7487488 Proceedings of the 2016 IEEE International Conference on Robotics and Automation (ICRA) Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Ramalingam, Srikumar Pillai, Sudeep Leonard, John J High-performance and tunable stereo reconstruction |
| title | High-performance and tunable stereo reconstruction |
| title_full | High-performance and tunable stereo reconstruction |
| title_fullStr | High-performance and tunable stereo reconstruction |
| title_full_unstemmed | High-performance and tunable stereo reconstruction |
| title_short | High-performance and tunable stereo reconstruction |
| title_sort | high performance and tunable stereo reconstruction |
| url | http://hdl.handle.net/1721.1/107456 https://orcid.org/0000-0001-7198-1772 https://orcid.org/0000-0002-8863-6550 |
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