Rigid registration of point clouds with partial overlaps is a longstanding
problem usually solved in two steps: (a) finding correspondences between the
point clouds; (b) filtering these correspondences to keep only the most
reliable ones to estimate the transformation. Recently, several deep nets have
been proposed to solve these steps jointly. We built upon these works and
propose PCAM: a neural network whose key element is a pointwise product of
cross-attention matrices that permits to mix both low-level geometric and
high-level contextual information to find point correspondences. These
cross-attention matrices also permits the exchange of context information
between the point clouds, at each layer, allowing the network construct better
matching features within the overlapping regions. The experiments show that
PCAM achieves state-of-the-art results among methods which, like us, solve
steps (a) and (b) jointly via deepnets. Our code and trained models are
available at this https URL

提出了一种名为 PCAM 的深度神经网络，用于点云对齐和配准任务，通过点对应和加权，将几何和语境信息结合，实现了空间特征的匹配，达到了最先进的结果。