Keypoint Cascade Voting for Point Cloud Based 6DoF Pose Estimation

TL;DR

This paper introduces RCVPose3D, a point cloud-based 6DoF pose estimation method that improves accuracy by separating segmentation and keypoint regression, incorporating pairwise constraints, and using a voter confidence score.

Contribution

It presents a novel cascaded keypoint voting architecture that enhances 6DoF pose estimation from point clouds without RGB data, achieving state-of-the-art results.

Findings

Achieves 74.5% on Occlusion LINEMOD dataset

Achieves 96.9% on YCB-Video dataset

Outperforms existing RGB and RGB-D methods

Abstract

We propose a novel keypoint voting 6DoF object pose estimation method, which takes pure unordered point cloud geometry as input without RGB information. The proposed cascaded keypoint voting method, called RCVPose3D, is based upon a novel architecture which separates the task of semantic segmentation from that of keypoint regression, thereby increasing the effectiveness of both and improving the ultimate performance. The method also introduces a pairwise constraint in between different keypoints to the loss function when regressing the quantity for keypoint estimation, which is shown to be effective, as well as a novel Voter Confident Score which enhances both the learning and inference stages. Our proposed RCVPose3D achieves state-of-the-art performance on the Occlusion LINEMOD (74.5%) and YCB-Video (96.9%) datasets, outperforming existing pure RGB and RGB-D based methods, as well as being competitive with RGB plus point cloud methods.