Category-Agnostic Pose Estimation for Point Clouds
<|reference_start|>Category-Agnostic Pose Estimation for Point Clouds: The goal of object pose estimation is to visually determine the pose of a specific object in the RGB-D input. Unfortunately, when faced with new categories, both instance-based and category-based methods are unable to deal with unseen objects of unseen categories, which is a challenge for pose estimation. To address this issue, this paper proposes a method to introduce geometric features for pose estimation of point clouds without requiring category information. The method is based only on the patch feature of the point cloud, a geometric feature with rotation invariance. After training without category information, our method achieves as good results as other category-based methods. Our method successfully achieved pose annotation of no category information instances on the CAMERA25 dataset and ModelNet40 dataset.<|reference_end|>
@article{liu2024category-agnostic,
    title={Category-Agnostic Pose Estimation for Point Clouds},
    author={Bowen Liu, Wei Liu, Siang Chen, Pengwei Xie and Guijin Wang},
    journal={arXiv preprint arXiv:2403.07437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07437},
    primaryClass={cs.CV}
}