mlsanityData to 7 Oct 2026

PCN: Point Completion Network

arXiv 1808.00671 ↗First posted 2 Aug 2018cs.CV cs.ROcited by 382

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Shape completion, the problem of estimating the complete geometry of objects from partial observations, lies at the core of many vision and robotics applications. In this work, we propose Point Completion Network (PCN), a novel learning-based approach for shape completion. Unlike existing shape completion methods, PCN directly operates on raw point clouds without any structural assumption (e.g. symmetry) or annotation (e.g. semantic class) about the underlying shape. It features a decoder design that enables the generation of fine-grained completions while maintaining a small number of parameters. Our experiments show that PCN produces dense, complete point clouds with realistic structures in the missing regions on inputs with various levels of incompleteness and noise, including cars from LiDAR scans in the KITTI dataset.

Key facts. Cited by 382 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 64; 2022: 94; 2023: 60; 2024: 63; 2025: 62; 2026: 39.

382 papers.

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  1. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
  2. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 284
  3. 2022AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generationcited by 208
  4. 2021Multimodal Virtual Point 3D Detectioncited by 166
  5. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 162
  6. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  7. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 111
  8. 2022ShapeFormer: Transformer-based Shape Completion via Sparse Representationcited by 106
  9. 2021Variational Relational Point Completion Networkcited by 103
  10. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  11. 2021SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generationcited by 93
  12. 2022POCO: Point Convolution for Surface Reconstructioncited by 91
38 more
  1. 2022SeedFormer: Patch Seeds based Point Cloud Completion with Upsample Transformercited by 83
  2. 20213D Semantic Scene Completion: a Surveycited by 74
  3. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  4. 2022ObjectFolder 2.0: A Multisensory Object Dataset for Sim2Real Transfercited by 73
  5. 2021SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elementscited by 72
  6. 2023Multiview Compressive Coding for 3D Reconstructioncited by 71
  7. 2021Unsupervised 3D Shape Completion through GAN Inversioncited by 70
  8. 2022ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimizationcited by 70
  9. 2022CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimationcited by 69
  10. 2021MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?cited by 68
  11. 2023AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware Transformerscited by 65
  12. 2021RGB-D Local Implicit Function for Depth Completion of Transparent Objectscited by 64
  13. 2023DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objectscited by 64
  14. 2022PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Pathscited by 57
  15. 2021Semantic Scene Completion via Integrating Instances and Scene in-the-Loopcited by 54
  16. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  17. 2022Surface Reconstruction from Point Clouds by Learning Predictive Context Priorscited by 54
  18. 2022Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fieldscited by 53
  19. 2021Cycle4Completion: Unpaired Point Cloud Completion using Cycle Transformation with Missing Region Codingcited by 50
  20. 2022SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Cloudscited by 49
  21. 2022Dynamic 3D Scene Analysis by Point Cloud Accumulationcited by 47
  22. 2021View-Guided Point Cloud Completioncited by 46
  23. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  24. 2022CAP-UDF: Learning Unsigned Distance Functions Progressively from Raw Point Clouds with Consistency-Aware Field Optimizationcited by 46
  25. 2021Style-based Point Generator with Adversarial Rendering for Point Cloud Completioncited by 45
  26. 20223D Shape Reconstruction from 2D Images with Disentangled Attribute Flowcited by 45
  27. 2022Equivariant Transporter Networkcited by 44
  28. 2022Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysiscited by 44
  29. 2022PointAttN: You Only Need Attention for Point Cloud Completioncited by 43
  30. 2023ProxyFormer: Proxy Alignment Assisted Point Cloud Completion with Missing Part Sensitive Transformercited by 43
  31. 2024Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learningcited by 43
  32. 2021GarmentNets: Category-Level Pose Estimation for Garments via Canonical Space Shape Completioncited by 41
  33. 2023The ObjectFolder Benchmark: Multisensory Learning with Neural and Real Objectscited by 40
  34. 2022Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformercited by 38
  35. 2023Point-Cloud Completion with Pretrained Text-to-image Diffusion Modelscited by 37
  36. 2022Cross-modal Learning for Image-Guided Point Cloud Shape Completioncited by 36
  37. 2021Active 3D Shape Reconstruction from Vision and Touchcited by 34
  38. 2023SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generatorcited by 34

50 most cited of 382 All 382 (JSON)