PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation

• URL: http://arxiv.org/abs/2108.09916v1
• Date: Mon, 23 Aug 2021 03:53:34 GMT
• Title: PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation
• Authors: Guangyuan Zhou, Huiqun Wang, Jiaxin Chen and Di Huang
• Abstract summary: RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations. This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN) It first introduces the Point Refinement Network (PRN) to polish 3D point clouds, recovering missing parts with noise removed. Subsequently, the Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to strengthen RGB-D combination.
• Score: 24.06845422193827
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: RGB-D based 6D pose estimation has recently achieved remarkable progress, but still suffers from two major limitations: (1) ineffective representation of depth data and (2) insufficient integration of different modalities. This paper proposes a novel deep learning approach, namely Graph Convolutional Network with Point Refinement (PR-GCN), to simultaneously address the issues above in a unified way. It first introduces the Point Refinement Network (PRN) to polish 3D point clouds, recovering missing parts with noise removed. Subsequently, the Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to strengthen RGB-D combination, which captures geometry-aware inter-modality correlation through local information propagation in the graph convolutional network. Extensive experiments are conducted on three widely used benchmarks, and state-of-the-art performance is reached. Besides, it is also shown that the proposed PRN and MMF-GCN modules are well generalized to other frameworks.

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