Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
<|reference_start|>Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration: 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.<|reference_end|>
@article{kang2024equi-gspr:,
    title={Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud
  Registration},
    author={Xueyang Kang and Zhaoliang Luan and Kourosh Khoshelham and Bing Wang},
    journal={arXiv preprint arXiv:2410.05729},
    year={2024},
    doi={10.1007/978-3-031-73235-5_9},
    archivePrefix={arXiv},
    eprint={2410.05729},
    primaryClass={cs.CV cs.AI}
}