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

Lei Zhou, Siyu Zhu, Zixin Luo, Tianwei Shen, Runze Zhang, Mingmin Zhen, Tian Fang, Long Quan; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 505-522

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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Zhou_2018_ECCV,
author = {Zhou, Lei and Zhu, Siyu and Luo, Zixin and Shen, Tianwei and Zhang, Runze and Zhen, Mingmin and Fang, Tian and Quan, Long},
title = {Learning and Matching Multi-View Descriptors for Registration of Point Clouds},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}
}