Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion

• URL: http://arxiv.org/abs/2204.09186v4
• Date: Mon, 24 Jul 2023 03:20:19 GMT
• Title: Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
• Authors: Zhaoxin Fan, Yulin He, Zhicheng Wang, Kejian Wu, Hongyan Liu and Jun He
• Abstract summary: Real-world sensors often produce incomplete, irregular, and noisy point clouds. This paper proposes RaPD, a novel semi-supervised point cloud completion method.
• Score: 10.649666758735663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, which is labor-intensive. This paper proposes RaPD, a novel semi-supervised point cloud completion method that reduces the need for paired datasets. RaPD utilizes a two-stage training scheme, where a deep semantic prior is learned in stage 1 from unpaired complete and incomplete point clouds, and a semi-supervised prior distillation process is introduced in stage 2 to train a completion network using only a small number of paired samples. Additionally, a self-supervised completion module is introduced to improve performance using unpaired incomplete point clouds. Experiments on multiple datasets show that RaPD outperforms previous methods in both homologous and heterologous scenarios.

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