SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

• URL: http://arxiv.org/abs/2307.09351v1
• Date: Tue, 18 Jul 2023 15:37:35 GMT
• Title: SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration
• Authors: Guiyu Zhao and Zhentao Guo and Xin Wang and Hongbin Ma
• Abstract summary: We introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. A new benchmark 3DMatch-noise with strong noise is introduced to evaluate our methods. It sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5% and 75.6% registration recall.
• Score: 5.641476052216014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud registration is to estimate a transformation to align point clouds collected in different perspectives. In learning-based point cloud registration, a robust descriptor is vital for high-accuracy registration. However, most methods are susceptible to noise and have poor generalization ability on unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization to encode initial features. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Under high-intensity noise, SphereNet increases the feature matching recall by more than 25 percentage points on 3DMatch-noise. In addition, it sets a new state-of-the-art performance for the 3DMatch and 3DLoMatch benchmarks with 93.5\% and 75.6\% registration recall and also has the best generalization ability on unseen datasets.

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