Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation

• URL: http://arxiv.org/abs/2208.14893v2
• Date: Thu, 1 Sep 2022 01:01:29 GMT
• Title: Improving RGB-D Point Cloud Registration by Learning Multi-scale Local Linear Transformation
• Authors: Ziming Wang, Xiaoliang Huo, Zhenghao Chen, Jing Zhang, Lu Sheng, Dong Xu
• Abstract summary: Point cloud registration aims at estimating the geometric transformation between two point cloud scans. Recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence. We propose a new Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local linear transformation.
• Score: 38.64501645574878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud registration aims at estimating the geometric transformation between two point cloud scans, in which point-wise correspondence estimation is the key to its success. In addition to previous methods that seek correspondences by hand-crafted or learnt geometric features, recent point cloud registration methods have tried to apply RGB-D data to achieve more accurate correspondence. However, it is not trivial to effectively fuse the geometric and visual information from these two distinctive modalities, especially for the registration problem. In this work, we propose a new Geometry-Aware Visual Feature Extractor (GAVE) that employs multi-scale local linear transformation to progressively fuse these two modalities, where the geometric features from the depth data act as the geometry-dependent convolution kernels to transform the visual features from the RGB data. The resultant visual-geometric features are in canonical feature spaces with alleviated visual dissimilarity caused by geometric changes, by which more reliable correspondence can be achieved. The proposed GAVE module can be readily plugged into recent RGB-D point cloud registration framework. Extensive experiments on 3D Match and ScanNet demonstrate that our method outperforms the state-of-the-art point cloud registration methods even without correspondence or pose supervision. The code is available at: https://github.com/514DNA/LLT.

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