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3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning

Abstract : 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.
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Preprints, Working Papers, ...
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https://hal-mines-paristech.archives-ouvertes.fr/hal-03203390
Contributor : François Goulette Connect in order to contact the contributor
Submitted on : Tuesday, April 20, 2021 - 6:00:26 PM
Last modification on : Thursday, April 22, 2021 - 3:11:58 AM

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  • HAL Id : hal-03203390, version 1
  • ARXIV : 2103.14533
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Sofiane Horache, Jean-Emmanuel Deschaud, Francois Goulette. 3D Point Cloud Registration with Multi-Scale Architecture and Self-supervised Fine-tuning. 2021. ⟨hal-03203390⟩

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