We propose RPSRNet - a novel end-to-end trainable deep neural network for
rigid point set registration. For this task, we use a novel $2^D$-tree
representation for the input point sets and a hierarchical deep feature
embedding in the neural network. An iterative transformation refinement module
in our network boosts the feature matching accuracy in the intermediate stages.
We achieve an inference speed of 12-15ms to register a pair of input point
clouds as large as 250K. Extensive evaluation on (i) KITTI LiDAR odometry and
(ii) ModelNet-40 datasets shows that our method outperforms prior
state-of-the-art methods - e.g., on the KITTI data set, DCP-v2 by1.3 and 1.5
times, and PointNetLK by 1.8 and 1.9 times better rotational and translational
accuracy respectively. Evaluation on ModelNet40 shows that RPSRNet is more
robust than other benchmark methods when the samples contain a significant
amount of noise and other disturbances. RPSRNet accurately registers point
clouds with non-uniform sampling densities, e.g., LiDAR data, which cannot be
processed by many existing deep-learning-based registration methods.

提出了一种新型的端到端可训练的深度神经网络 RPSRNet，用于刚性点集配准，使用了 $2^D$-tree 表示输入点集，并在神经网络中使用分层深度特征嵌入，包括迭代变换细化模块，可以有效地处理噪声和非均匀采样密度的 LiDAR 等数据，相比之前的最新方法在点云配准上具有更好的准确性。