One-Nearest Neighborhood Guides Inlier Estimation for Unsupervised Point Cloud Registration

TL;DR

This paper introduces a novel inlier estimation method for unsupervised point cloud registration that leverages geometric structure consistency between source and reference point clouds, improving registration accuracy without supervision.

Contribution

It proposes a new inlier estimation approach using 1-NN generated reference point clouds and geometric structure consistency, enhancing unsupervised registration performance.

Findings

Improved registration accuracy on synthetic datasets

Effective inlier confidence scoring mechanism

Robust performance on real-world datasets

Abstract

The precision of unsupervised point cloud registration methods is typically limited by the lack of reliable inlier estimation and self-supervised signal, especially in partially overlapping scenarios. In this paper, we propose an effective inlier estimation method for unsupervised point cloud registration by capturing geometric structure consistency between the source point cloud and its corresponding reference point cloud copy. Specifically, to obtain a high quality reference point cloud copy, an One-Nearest Neighborhood (1-NN) point cloud is generated by input point cloud. This facilitates matching map construction and allows for integrating dual neighborhood matching scores of 1-NN point cloud and input point cloud to improve matching confidence. Benefiting from the high quality reference copy, we argue that the neighborhood graph formed by inlier and its neighborhood should have consistency between source point cloud and its corresponding reference copy. Based on this observation, we construct transformation-invariant geometric structure representations and capture geometric structure consistency to score the inlier confidence for estimated correspondences between source point cloud and its reference copy. This strategy can simultaneously provide the reliable self-supervised signal for model optimization. Finally, we further calculate transformation estimation by the weighted SVD algorithm with the estimated correspondences and corresponding inlier confidence. We train the proposed model in an unsupervised manner, and extensive experiments on synthetic and real-world datasets illustrate the effectiveness of the proposed method.