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.
@article{arxiv.2403.07437,
title = {Category-Agnostic Pose Estimation for Point Clouds},
author = {Bowen Liu and Wei Liu and Siang Chen and Pengwei Xie and Guijin Wang},
journal= {arXiv preprint arXiv:2403.07437},
year = {2024}
}