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：本文引入了基于深层学习的3D点云登记新方法。 结构由三个不同的组组成:(一) 由一个基于图表的编码器组成的编码器,该编码器对每个点的近邻进行编码,以及一个记录表面正常值变化的注意机制。通过突出同一组点之间的注意和两组点之间的注意来改进这些描述器。 (二) 利用辛克霍恩算法估计通信矩阵的匹配程序。 (三) 最后,由RANSAC使用通信矩阵Kc最佳分数来计算两点云之间的僵硬转换。我们在模型Net40数据集上进行实验,我们拟议的结构显示非常有希望的结果,在大多数模拟配置中,表现优于最先进的方法,包括与高斯噪音部分重叠和数据增强。</s>