Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)

TL;DR

This paper introduces a deep graph matching framework for 3D point cloud registration that effectively handles outliers and improves correspondence accuracy by considering local and global geometric structures.

Contribution

It proposes a novel deep graph matching approach with a transformer-based edge generation, achieving state-of-the-art results in point cloud registration.

Findings

Outperforms existing methods on benchmark datasets

Effectively handles outliers in point clouds

Incorporates global structure in correspondence matching

Abstract

3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, these methods are sensitive to outliers, which lead to more incorrect correspondences. In this paper, we propose a novel deep graph matching-based framework for point cloud registration. Specifically, we first transform point clouds into graphs and extract deep features for each point. Then, we develop a module based on deep graph matching to calculate a soft correspondence matrix. By using graph matching, not only the local geometry of each point but also its structure and topology in a larger range are considered in establishing correspondences, so that more correct correspondences are found. We train the network with a loss directly defined on the correspondences, and in the test stage the soft correspondences are transformed into hard one-to-one correspondences so that registration can be performed by a correspondence-based solver. Furthermore, we introduce a transformer-based method to generate edges for graph construction, which further improves the quality of the correspondences. Extensive experiments on object-level and scene-level benchmark datasets show that the proposed method achieves state-of-the-art performance. The code is available at: \href{https://github.com/fukexue/RGM}{https://github.com/fukexue/RGM}.