提出可处理点云旋转对称性的图网络，提升稀疏点云配准精度与效率。
Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration
- 基于SE(3)消息传递构建旋转等变图网络，增强对点云对称性的建模能力。
- 在3DMatch和KITTI数据集上优于主流方法，且模型复杂度更低。
- 适合需要高精度点云配准的自动驾驶、三维重建等场景。
点云配准是三维对齐与重建的基础任务。尽管传统与基于学习的方法均取得成功，但对点云数据固有的对称性（如旋转等变性）的关注仍不足，导致模型学习效率低，需更多训练数据且模型复杂度高。为此，我们提出一种嵌入局部球面欧氏3D等变性质的图神经网络模型，通过基于SE(3)的消息传递机制实现等变传播。模型由描述符模块、等变图层、匹配相似性计算及最终回归层组成，其模块化设计支持稀疏输入点，并可便捷地使用自训练或预训练几何特征描述符初始化。在3DMatch和KITTI数据集上的实验表明，本模型性能显著优于现有先进方法，同时保持较低的模型复杂度。
原文摘要 · Abstract (English)
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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