DeepBBS: Deep Best Buddies for Point Cloud Registration

TL;DR

DeepBBS introduces a novel point cloud registration method leveraging Best Buddy distances to improve matching accuracy, especially for partial and unseen object categories, outperforming previous approaches.

Contribution

The paper presents DeepBBS, a new learning-based registration technique that incorporates Best Buddy criteria to enhance matching robustness and generalization.

Findings

Improved registration accuracy on partial shapes.

Effective in unseen object categories.

Outperforms previous methods in experiments.

Abstract

Recently, several deep learning approaches have been proposed for point cloud registration. These methods train a network to generate a representation that helps finding matching points in two 3D point clouds. Finding good matches allows them to calculate the transformation between the point clouds accurately. Two challenges of these techniques are dealing with occlusions and generalizing to objects of classes unseen during training. This work proposes DeepBBS, a novel method for learning a representation that takes into account the best buddy distance between points during training. Best Buddies (i.e., mutual nearest neighbors) are pairs of points nearest to each other. The Best Buddies criterion is a strong indication for correct matches that, in turn, leads to accurate registration. Our experiments show improved performance compared to previous methods. In particular, our learned representation leads to an accurate registration for partial shapes and in unseen categories.