Category-Agnostic Pose Estimation for Point Clouds
Bowen Liu Wei Liu Siang Chen Pengwei Xie Guijin Wang
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.