  We present MS-SVConv, a fast multi-scale deep neural network that outputs
features from point clouds for 3D registration between two scenes. We compute
features using a 3D sparse voxel convolutional network on a point cloud at
different scales and then fuse the features through fully-connected layers.
With supervised learning, we show significant improvements compared to
state-of-the-art methods on the competitive and well-known 3DMatch benchmark.
We also achieve a better generalization through different source and target
datasets, with very fast computation. Finally, we present a strategy to
fine-tune MS-SVConv on unknown datasets in a self-supervised way, which leads
to state-of-the-art results on ETH and TUM datasets.
