Robust Partial-to-Partial Point Cloud Registration in a Full Range

• URL: http://arxiv.org/abs/2111.15606v1
• Date: Tue, 30 Nov 2021 17:56:24 GMT
• Title: Robust Partial-to-Partial Point Cloud Registration in a Full Range
• Authors: Liang Pan, Zhongang Cai, and Ziwei Liu
• Abstract summary: We propose Graph Matching Consensus Network (GMCNet), which estimates pose-invariant correspondences for fullrange 1 Partial-to-Partial point cloud Registration (PPR) GMCNet encodes point descriptors for each point cloud individually without using crosscontextual information, or ground truth correspondences for training.
• Score: 12.86951061306046
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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