Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
Zhaoxin Fan, Yulin He, Zhicheng Wang, Kejian Wu, Hongyan Liu, Jun He
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence
Main Track. Pages 726-735.
https://doi.org/10.24963/ijcai.2023/81
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.
Keywords:
Computer Vision: CV: 3D computer vision