Point cloud registration for 3D objects is a challenging task due to sparse
and noisy measurements, incomplete observations and large transformations. In
this work, we propose \textbf{G}raph \textbf{M}atching \textbf{C}onsensus
\textbf{Net}work (\textbf{GMCNet}), which estimates pose-invariant
correspondences for full-range Partial-to-Partial point cloud Registration
(PPR) in the object-level registration scenario. To encode robust point
descriptors, \textbf{1)} we first comprehensively investigate
transformation-robustness and noise-resilience of various geometric features.
\textbf{2)} Then, we employ a novel {T}ransformation-robust {P}oint
{T}ransformer (\textbf{TPT}) module to adaptively aggregate local features
regarding the structural relations, which takes advantage from both handcrafted
rotation-invariant ({\textit{RI}}) features and noise-resilient spatial
coordinates. \textbf{3)} Based on a synergy of hierarchical graph networks and
graphical modeling, we propose the {H}ierarchical {G}raphical {M}odeling
(\textbf{HGM}) architecture to encode robust descriptors consisting of i) a
unary term learned from {\textit{RI}} features; and ii) multiple smoothness
terms encoded from neighboring point relations at different scales through our
TPT modules. Moreover, we construct a challenging PPR dataset (\textbf{MVP-RG})
based on the recent MVP dataset that features high-quality scans. Extensive
experiments show that GMCNet outperforms previous state-of-the-art methods for
PPR. Notably, GMCNet encodes point descriptors for each point cloud
individually without using cross-contextual information, or ground truth
correspondences for training. Our code and datasets are available at:
this https URL

GMCNet 使用包括几何特征、自适应局部特征和基于图表征的统计建模等多种策略实现了全范围的点云配准，在点云配准方面超过了以往的最先进方法。