Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration

• URL: http://arxiv.org/abs/2410.05729v1
• Date: Tue, 8 Oct 2024 06:48:01 GMT
• Title: Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
• Authors: Xueyang Kang, Zhaoliang Luan, Kourosh Khoshelham, Bing Wang,
• Abstract summary: We propose a graph neural network model embedded with a local Spherical Euclidean 3D equivariance property through SE(3) message passing based propagation. Our model is composed mainly of a descriptor module, equivariant graph layers, match similarity, and the final regression layers. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches.
• Score: 2.814748676983944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Point cloud registration is a foundational task for 3D alignment and reconstruction applications. While both traditional and learning-based registration approaches have succeeded, leveraging the intrinsic symmetry of point cloud data, including rotation equivariance, has received insufficient attention. This prohibits the model from learning effectively, resulting in a requirement for more training data and increased model complexity. To address these challenges, we propose a graph neural network model embedded with a local Spherical Euclidean 3D equivariance property through SE(3) message passing based propagation. Our model is composed mainly of a descriptor module, equivariant graph layers, match similarity, and the final regression layers. Such modular design enables us to utilize sparsely sampled input points and initialize the descriptor by self-trained or pre-trained geometric feature descriptors easily. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches, while the model complexity remains relatively low at the same time.

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