SRPCN: Structure Retrieval based Point Completion Network
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Abstract (arXiv)
Given partial objects and some complete ones as references, point cloud completion aims to recover authentic shapes. However, existing methods pay little attention to general shapes, which leads to the poor authenticity of completion results. Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability. Considering that a partial point cloud is a subset of the corresponding complete one, we regard them as different samples of the same distribution and propose Structure Retrieval based Point Completion Network (SRPCN). It first uses k-means clustering to extract structure points and disperses them into distributions, and then KL Divergence is used as a metric to find the complete structure point cloud that best matches the input in a database. Finally, a PCN-like decoder network is adopted to generate the final results based on the retrieved structure point clouds. As structure plays an important role in describing the general shape of an object and the proposed structure retrieval method is robust to missing patterns, experiments show that our method can generate more authentic results and has a stronger generalization ability.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 26 of its 39 references that are in the map. First posted on arXiv 6 Feb 2022.
Builds on
26 of 39 references.
References · 26
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
- 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,205
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- 2018PointCNN: Convolution On X-Transformed Pointscited by 764
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- 2018PCN: Point Completion Networkcited by 384
- 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
14 more
- 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
- 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
- 2017Learning Efficient Point Cloud Generation for Dense 3D Object Reconstructioncited by 133
- 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
- 2020Cascaded Refinement Network for Point Cloud Completioncited by 112
- 2020Point Cloud Completion by Skip-attention Network with Hierarchical Foldingcited by 106
- 2017ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scanscited by 103
- 2018Multiresolution Tree Networks for 3D Point Cloud Processingcited by 91
- 2016Shape Completion Enabled Robotic Graspingcited by 79
- 2020Detail Preserved Point Cloud Completion via Separated Feature Aggregationcited by 70
- 2019Unpaired Point Cloud Completion on Real Scans using Adversarial Trainingcited by 66
- 2020Multimodal Shape Completion via Conditional Generative Adversarial Networkscited by 61
- 2019RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completioncited by 58
- 2020SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classificationcited by 33
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1.
2 papers.