Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation
<|reference_start|>Resolving Symmetry Ambiguity in Correspondence-based Methods for Instance-level Object Pose Estimation: 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.<|reference_end|>
@article{lin2024resolving,
    title={Resolving Symmetry Ambiguity in Correspondence-based Methods for
  Instance-level Object Pose Estimation},
    author={Yongliang Lin, Yongzhi Su, Sandeep Inuganti, Yan Di, Naeem
  Ajilforoushan, Hanqing Yang, Yu Zhang and Jason Rambach},
    journal={arXiv preprint arXiv:2405.10557},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10557},
    primaryClass={cs.CV}
}