Learning and Matching Multi-View Descriptors for Registration of Point Clouds

TL;DR

This paper introduces a multi-view local descriptor learned from images for 3D keypoints and a robust matching approach using belief propagation, significantly improving point cloud registration accuracy.

Contribution

It proposes a novel multi-view descriptor and a robust matching method, enhancing registration performance over existing techniques.

Findings

Improved registration accuracy on public datasets.

Robust outlier rejection in matching process.

Superior performance compared to existing descriptors and methods.

Abstract

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods.