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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10557 |
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| _version_ | 1866917668671455232 |
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| author | Lin, Yongliang Su, Yongzhi Inuganti, Sandeep Di, Yan Ajilforoushan, Naeem Yang, Hanqing Zhang, Yu Rambach, Jason |
| author_facet | Lin, Yongliang Su, Yongzhi Inuganti, Sandeep Di, Yan Ajilforoushan, Naeem Yang, Hanqing Zhang, Yu Rambach, Jason |
| contents | Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D object surface vertices. However, the utilization of one-to-one correspondences introduces ambiguity for symmetric objects. To address this, we propose SymCode, a symmetry-aware surface encoding that encodes the object surface vertices based on one-to-many correspondences, eliminating the problem of one-to-one correspondence ambiguity. We also introduce SymNet, a fast end-to-end network that directly regresses the 6D pose parameters without solving a PnP problem. We demonstrate faster runtime and comparable accuracy achieved by our method on the T-LESS and IC-BIN benchmarks of mostly symmetric objects. Our source code will be released upon acceptance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10557 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation Lin, Yongliang Su, Yongzhi Inuganti, Sandeep Di, Yan Ajilforoushan, Naeem Yang, Hanqing Zhang, Yu Rambach, Jason Computer Vision and Pattern Recognition Estimating the 6D pose of an object from a single RGB image is a critical task that becomes additionally challenging when dealing with symmetric objects. Recent approaches typically establish one-to-one correspondences between image pixels and 3D object surface vertices. However, the utilization of one-to-one correspondences introduces ambiguity for symmetric objects. To address this, we propose SymCode, a symmetry-aware surface encoding that encodes the object surface vertices based on one-to-many correspondences, eliminating the problem of one-to-one correspondence ambiguity. We also introduce SymNet, a fast end-to-end network that directly regresses the 6D pose parameters without solving a PnP problem. We demonstrate faster runtime and comparable accuracy achieved by our method on the T-LESS and IC-BIN benchmarks of mostly symmetric objects. Our source code will be released upon acceptance. |
| title | Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.10557 |