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We propose a method for generalizing deep learning for 3D point cloudregistration on new, totally different datasets. It is based on two components,MS-SVConv and UDGE. Using Multi-Scale Sparse Voxel Convolution, MS-SVConv is afast deep neural network that outputs the descriptors from point clouds for 3Dregistration between two scenes. UDGE is an algorithm for transferring deepnetworks on unknown datasets in a unsupervised way. The interest of theproposed 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 worldregistration datasets such as 3DMatch, ETH and TUM. The code is publiclyavailable at https://github.com/humanpose1/MS-SVConv .

3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning