Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
This work addresses the labor-intensive need for large paired datasets in point cloud completion, which is crucial for real-world sensor applications.
The paper tackles the problem of point cloud completion with limited paired training data by proposing RaPD, a semi-supervised method that uses a two-stage training scheme with prior distillation and self-supervision, achieving superior performance on multiple datasets compared to previous methods.
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.