Point cloud registration is a key problem for computer vision applied to
robotics, medical imaging, and other applications. This problem involves
finding a rigid transformation from one point cloud into another so that they
align. Iterative Closest Point (ICP) and its variants provide simple and
easily-implemented iterative methods for this task, but these algorithms can
converge to spurious local optima. To address local optima and other
difficulties in the ICP pipeline, we propose a learning-based method, titled
Deep Closest Point (DCP), inspired by recent techniques in computer vision and
natural language processing. Our model consists of three parts: a point cloud
embedding network, an attention-based module combined with a pointer generation
layer, to approximate combinatorial matching, and a differentiable singular
value decomposition (SVD) layer to extract the final rigid transformation. We
train our model end-to-end on the ModelNet40 dataset and show in several
settings that it performs better than ICP, its variants (e.g., Go-ICP, FGR),
and the recently-proposed learning-based method PointNetLK. Beyond providing a
state-of-the-art registration technique, we evaluate the suitability of our
learned features transferred to unseen objects. We also provide preliminary
analysis of our learned model to help understand whether domain-specific and/or
global features facilitate rigid registration.

本文提出了一种基于学习的方法 Deep Closest Point，用于点云配准问题，通过点云嵌入网络、基于注意力机制的组合匹配模块和可微分的奇异值分解层，实现最终的刚性变换。实验证明该方法优于传统算法 Iterative Closest Point 及其变种，以及最近提出的基于学习的方法 PointNetLK，同时能有效迁移学到的特征。