Robust Partial-to-Partial Point Cloud Registration in a Full Range
release_oot7yzovvfganovuxpuppgnxjq
by
Liang Pan, Zhongang Cai, Ziwei Liu
2021
Abstract
Point cloud registration for 3D objects is very challenging due to sparse and
noisy measurements, incomplete observations and large transformations. In this
work, we propose Graph Matching Consensus Network (GMCNet), which estimates
pose-invariant correspondences for fullrange 1 Partial-to-Partial point cloud
Registration (PPR). To encode robust point descriptors, 1) we first
comprehensively investigate transformation-robustness and noiseresilience of
various geometric features. 2) Then, we employ a novel Transformation-robust
Point Transformer (TPT) modules to adaptively aggregate local features
regarding the structural relations, which takes advantage from both handcrafted
rotation-invariant (RI) features and noise-resilient spatial coordinates. 3)
Based on a synergy of hierarchical graph networks and graphical modeling, we
propose the Hierarchical Graphical Modeling (HGM) architecture to encode robust
descriptors consisting of i) a unary term learned from 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 (MVP-RG) with virtual scans. Extensive experiments show that GMCNet
outperforms previous state-of-the-art methods for PPR. Remarkably, GMCNet
encodes point descriptors for each point cloud individually without using
crosscontextual information, or ground truth correspondences for training. Our
code and datasets will be available at https://github.com/paul007pl/GMCNet.
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