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。该架构由三个独立模块组成：（i）编码器，包含基于卷积图描述符和注意力机制，前者编码每个点的局部邻域信息，后者编码表面法向量的变化。通过突出同一集合内点之间的注意力，再扩展到两个点集之间的注意力，对描述符进行精化；（ii）匹配过程，使用Sinkhorn算法估计对应关系矩阵；（iii）最后，利用对应矩阵中Kc个最佳得分，通过RANSAC算法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表现出非常优异的性能，在大多数模拟配置（包括部分重叠和含高斯噪声的数据增强）中均优于现有最先进方法。