Papers › 3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning
3D Point Cloud Registration with Multi-Scale Architecture and Unsupervised Transfer Learning
Sofiane Horache, Jean-Emmanuel Deschaud, François Goulette
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 .
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Point Cloud Registration | 3DMatch Benchmark | MS-SVConv | Feature Matching Recall | 98.4 | #2 of 15 | Archive leaderboard | report |
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Methods
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