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Morphing and Sampling Network for Dense Point Cloud Completion

arXiv 1912.00280 ↗First posted 30 Nov 2019cs.CVcited by 132

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Abstract (arXiv)

3D point cloud completion, the task of inferring the complete geometric shape from a partial point cloud, has been attracting attention in the community. For acquiring high-fidelity dense point clouds and avoiding uneven distribution, blurred details, or structural loss of existing methods' results, we propose a novel approach to complete the partial point cloud in two stages. Specifically, in the first stage, the approach predicts a complete but coarse-grained point cloud with a collection of parametric surface elements. Then, in the second stage, it merges the coarse-grained prediction with the input point cloud by a novel sampling algorithm. Our method utilizes a joint loss function to guide the distribution of the points. Extensive experiments verify the effectiveness of our method and demonstrate that it outperforms the existing methods in both the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

Key facts. Cited by 132 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 30; 2022: 38; 2023: 23; 2024: 16; 2025: 20; 2026: 5.

132 papers.

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  1. 2023One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimizationcited by 560
  2. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  3. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 286
  4. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 164
  5. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 112
  6. 2021Variational Relational Point Completion Networkcited by 104
  7. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  8. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  9. 2021Unsupervised 3D Shape Completion through GAN Inversioncited by 70
  10. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  11. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 67
  12. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
38 more
  1. 2021Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Codingcited by 50
  2. 2022Low-Rank Tensor Function Representation for Multi-Dimensional Data Recoverycited by 46
  3. 20223D Shape Reconstruction from 2D Images with Disentangled Attribute Flowcited by 45
  4. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  5. 2023ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformercited by 43
  6. 2021Long-Horizon Manipulation of Unknown Objects via Task and Motion Planning with Estimated Affordancescited by 40
  7. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  8. 2021Point-Based Modeling of Human Clothingcited by 37
  9. 2022Autoregressive 3D Shape Generation via Canonical Mappingcited by 37
  10. 2022Cross-modal Learning for Image-Guided Point Cloud Shape Completioncited by 36
  11. 2023SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generatorcited by 34
  12. 2021Voxel-based Network for Shape Completion by Leveraging Edge Generationcited by 32
  13. 2021Orienting Point Clouds with Dipole Propagationcited by 31
  14. 2022Topologically-Aware Deformation Fields for Single-View 3D Reconstructioncited by 29
  15. 2021MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancycited by 28
  16. 2022LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned Keypointscited by 27
  17. 2022Imperceptible and Robust Backdoor Attack in 3D Point Cloudcited by 25
  18. 2023Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Modelscited by 25
  19. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  20. 2022CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completioncited by 22
  21. 2024CRA-PCN: Point Cloud Completion with Intra- and Inter-level Cross-Resolution Transformerscited by 21
  22. 2022FBNet: Feedback Network for Point Cloud Completioncited by 20
  23. 2021Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wildcited by 19
  24. 2022From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Predictioncited by 19
  25. 2021ME-PCN: Point Completion Conditioned on Mask Emptinesscited by 18
  26. 2021Denoise and Contrast for Category Agnostic Shape Completioncited by 17
  27. 2022Learning a Structured Latent Space for Unsupervised Point Cloud Completioncited by 17
  28. 2021Self-Supervised Point Cloud Completion via Inpaintingcited by 16
  29. 2022Learning Local Displacements for Point Cloud Completioncited by 16
  30. 2024PCDreamer: Point Cloud Completion Through Multi-view Diffusion Priorscited by 16
  31. 2024Atlas Gaussians Diffusion for 3D Generationcited by 15
  32. 2025SymmCompletion: High-Fidelity and High-Consistency Point Cloud Completion with Symmetry Guidancecited by 15
  33. 2022SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Samplingcited by 14
  34. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  35. 20233DSGrasp: 3D Shape-Completion for Robotic Graspcited by 13
  36. 2021HyperPocket: Generative Point Cloud Completioncited by 12
  37. 2022CasFusionNet: A Cascaded Network for Point Cloud Semantic Scene Completion by Dense Feature Fusioncited by 11
  38. 2021A Deep Signed Directional Distance Function for Object Shape Representationcited by 10

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