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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