mlsanityData to 7 Oct 2026

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

arXiv 2204.09186 ↗First posted 20 Apr 2022cs.CVcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 20 Apr 2022.

Builds on

30 of 53 references.

References · 30

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18 more
  1. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
  2. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
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Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 1; 2025: 1.

5 papers.

  1. 2023EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animationcited by 141
  2. 2022Bridging the Domain Gap for Multi-Agent Perceptioncited by 60
  3. 2023PMMTalk: Speech-Driven 3D Facial Animation from Complementary Pseudo Multi-modal Featurescited by 6
  4. 2024Hyperbolic Chamfer Distance for Point Cloud Completion and Beyondcited by 3
  5. 2025CasPoinTr: Point Cloud Completion with Cascaded Networks and Knowledge Distillationcited by 0