This paper introduces a new method for 3D point cloud registration based on deep learning. The architecture is composed of three distinct blocs: (i) an encoder composed of a convolutional graph-based descriptor that encodes the immediate neighbourhood of each point and an attention mechanism that encodes the variations of the surface normals. Such descriptors are refined by highlighting attention between the points of the same set and then between the points of the two sets. (ii) a matching process that estimates a matrix of correspondences using the Sinkhorn algorithm. (iii) Finally, the rigid transformation between the two point clouds is calculated by RANSAC using the Kc best scores from the correspondence matrix. We conduct experiments on the ModelNet40 dataset, and our proposed architecture shows very promising results, outperforming state-of-the-art methods in most of the simulated configurations, including partial overlap and data augmentation with Gaussian noise.

翻译：本文提出了一种基于深度学习的3D点云配准新方法。该架构由三个不同模块组成：（i）编码器，包含基于图卷积的描述子（用于编码每个点的邻域信息）和注意力机制（用于编码表面法向量的变化）。这些描述子通过增强同一集合内点之间的注意力，进而增强两个集合间点之间的注意力得到优化。（ii）匹配过程，利用Sinkhorn算法估计对应关系矩阵。（iii）最后，通过RANSAC算法基于对应关系矩阵中Kc个最佳得分计算两个点云之间的刚体变换。我们在ModelNet40数据集上进行了实验，结果表明所提出的架构在大部分模拟配置下（包括部分重叠及添加高斯噪声的数据增强）均展现出显著优势，性能优于现有主流方法。