@inproceedings{
zhang2024partlevel,
title={Part-level Reconstruction for Self-Supervised Category-level 6D Object Pose Estimation with Coarse-to-Fine Correspondence Optimization},
author={Zerui Zhang and Jun Yu and Liangxian Cui and Qiang Ling and TianyuLiu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=D4cTfcaHOc}
}
Self-supervised category-level 6D pose estimation stands as a fundamental task in computer vision. Nonetheless, existing methods encounter the following challenges: 1) They are impacted by the many-to-one ambiguity in the correspondences between pixels and point clouds. 2) Existing networks struggle to reconstruct precise object models due to the significant part-level shape variations among specific categories. To address these issues, we propose a novel method based on a Coarse-to-Fine Correspondence Optimization (\textbf{CFCO}) module and a Part-level Shape Reconstruction (\textbf{PSR}) module. In the \textbf{CFCO} module, we employ Hungarian matching to generate one-to-one pseudo labels at both region and pixel levels, providing explicit supervision for the corresponding similarity matrices. In the \textbf{PSR} module, we introduce a part-level discrete shape memory to capture more fine-grained shape variations of different objects and utilize it to perform precise reconstruction. We evaluate our method on the REAL275 and WILD6D datasets. Extensive experiments demonstrate that our method outperforms existing methods, achieving new state-of-the-art results.
Part-level Reconstruction for Self-Supervised Category-level 6D Object Pose Estimation with Coarse-to-Fine Correspondence Optimization
Zerui Zhang Jun Yu Liangxian Cui Qiang Ling TianyuLiu