We propose a method for generalizing deep learning for 3D point cloud registration on new, totally different datasets. It is based on two components, MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MS-SVConv is a fast deep neural network that outputs the descriptors from point clouds for 3D registration between two scenes. UDGE is an algorithm for transferring deep networks on unknown datasets in a unsupervised way. The interest of the proposed method appears while using the two components, MS-SVConv and UDGE, together as a whole, which leads to state-of-the-art results on real world registration datasets such as 3DMatch, ETH and TUM. The code is publicly available at https://github.com/humanpose1/MS-SVConv .
@article{arxiv.2103.14533,
title = {3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning},
author = {Sofiane Horache and Jean-Emmanuel Deschaud and François Goulette},
journal= {arXiv preprint arXiv:2103.14533},
year = {2021}
}