mlsanityData to 7 Oct 2026

Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis

arXiv 2203.03311 ↗First posted 7 Mar 2022cs.CVcited by 44

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Abstract (arXiv)

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications in 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, convolution-based, graph-based, and generative model-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Builds on 85 of its 145 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

85 of 145 references.

References · 85

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  3. 2022Generative Adversarial Networkscited by 5,603
  4. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,229
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  8. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  9. 2020Point Transformercited by 1,462
  10. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  11. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  12. 2018Image Transformercited by 770
38 more
  1. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  2. 2020PCT: Point cloud transformercited by 730
  3. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  4. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  5. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  6. 2018PCN: Point Completion Networkcited by 382
  7. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  8. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  9. 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
  10. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 284
  11. 2020Deep Continuous Fusion for Multi-Sensor 3D Object Detectioncited by 263
  12. 2019What Do Single-view 3D Reconstruction Networks Learn?cited by 259
  13. 2020Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completioncited by 222
  14. 2016Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesiscited by 219
  15. 2018PU-Net: Point Cloud Upsampling Networkcited by 208
  16. 20203D Object Detection with Pointformercited by 194
  17. 2020LMSCNet: Lightweight Multiscale 3D Semantic Completioncited by 187
  18. 2020Unsupervised Point Cloud Pre-Training via Occlusion Completioncited by 186
  19. 2020GRNet: Gridding Residual Network for Dense Point Cloud Completioncited by 174
  20. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 162
  21. 2021SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformercited by 162
  22. 2020PF-Net: Point Fractal Network for 3D Point Cloud Completioncited by 146
  23. 2019Morphing and Sampling Network for Dense Point Cloud Completioncited by 132
  24. 2017Pointwise Convolutional Neural Networkscited by 126
  25. 2020Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Cloudscited by 116
  26. 2020Cascaded Refinement Network for Point Cloud Completioncited by 112
  27. 2021Density-aware Chamfer Distance as a Comprehensive Metric for Point Cloud Completioncited by 111
  28. 20183D Point Capsule Networkscited by 106
  29. 2020Point Cloud Completion by Skip-attention Network with Hierarchical Foldingcited by 106
  30. 2018PointGrow: Autoregressively Learned Point Cloud Generation with Self-Attentioncited by 105
  31. 2021Variational Relational Point Completion Networkcited by 103
  32. 2020S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Cloudscited by 102
  33. 2019PU-GCN: Point Cloud Upsampling using Graph Convolutional Networkscited by 100
  34. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  35. 2020PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Pathscited by 97
  36. 2019Unsupervised Multi-Task Feature Learning on Point Cloudscited by 89
  37. 2017High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inferencecited by 87
  38. 2021A comprehensive survey on point cloud registrationcited by 84

50 most cited of 85 All 85 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 13; 2024: 15; 2025: 12; 2026: 1.

44 papers.

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  1. 2023SCPNet: Semantic Scene Completion on Point Cloudcited by 80
  2. 2024When LLMs step into the 3D World: A Survey and Meta-Analysis of 3D Tasks via Multi-modal Large Language Modelscited by 41
  3. 2022Cross-modal Learning for Image-Guided Point Cloud Shape Completioncited by 36
  4. 2023Advancements in Point Cloud Data Augmentation for Deep Learning: A Surveycited by 24
  5. 2023Deep Learning-based 3D Point Cloud Classification: A Systematic Survey and Outlookcited by 24
  6. 2023LiDAR-based 4D Occupancy Completion and Forecastingcited by 23
  7. 2022CSDN: Cross-modal Shape-transfer Dual-refinement Network for Point Cloud Completioncited by 22
  8. 2023Fantastic Breaks: A Dataset of Paired 3D Scans of Real-World Broken Objects and Their Complete Counterpartscited by 18
  9. 2024P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoisingcited by 18
  10. 2023Point2SSM: Learning Morphological Variations of Anatomies from Point Cloudcited by 14
  11. 2023Occluded Person Re-Identification with Deep Learning: A Survey and Perspectivescited by 13
  12. 2023Can point cloud networks learn statistical shape models of anatomies?cited by 9
32 more
  1. 2024Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completioncited by 9
  2. 2023Fine-grained Text and Image Guided Point Cloud Completion with CLIP Modelcited by 5
  3. 2024Building-PCC: Building Point Cloud Completion Benchmarkscited by 5
  4. 2022Shape Completion with Points in the Shadowcited by 4
  5. 2024Weakly Supervised Bayesian Shape Modeling from Unsegmented Medical Imagescited by 4
  6. 2024Deep Learning for 3D Point Cloud Enhancement: A Surveycited by 4
  7. 2024Revisiting Point Cloud Completion: Are We Ready For The Real-World?cited by 4
  8. 2025DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flowscited by 4
  9. 2024Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Posecited by 3
  10. 2024High-throughput 3D shape completion of potato tubers on a harvestercited by 2
  11. 2025MambaTron: Efficient Cross-Modal Point Cloud Enhancement using Aggregate Selective State Space Modelingcited by 2
  12. 2025Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splattingcited by 2
  13. 2025VBCD: A Voxel-Based Framework for Personalized Dental Crown Designcited by 2
  14. 2026MGPC: Multimodal Network for Generalizable Point Cloud Completion With Modality Dropout and Progressive Decodingcited by 2
  15. 20233D GANs and Latent Space: A comprehensive surveycited by 1
  16. 2023MLP-AIR: An Efficient MLP-Based Method for Actor Interaction Relation Learning in Group Activity Recognitioncited by 1
  17. 2023PU-Ray: Domain-Independent Point Cloud Upsampling via Ray Marching on Neural Implicit Surfacecited by 1
  18. 2024No Captions, No Problem: Captionless 3D-CLIP Alignment with Hard Negatives via CLIP Knowledge and LLMscited by 1
  19. 2024Enhancing Performance of Point Cloud Completion Networks with Consistency Losscited by 1
  20. 2024MBPU: A Plug-and-Play State Space Model for Point Cloud Upsamping with Fast Point Renderingcited by 1
  21. 2025SUM Parts: Benchmarking Part-Level Semantic Segmentation of Urban Meshescited by 1
  22. 2025SurfFill: Completion of LiDAR Point Clouds via Gaussian Surfel Splattingcited by 1
  23. 2023NeuSD: Surface Completion with Multi-View Text-to-Image Diffusioncited by 0
  24. 2024Terrain Point Cloud Inpainting via Signal Decompositioncited by 0
  25. 2024Gap Completion in Point Cloud Scene occluded by Vehicles using SGC-Netcited by 0
  26. 2024SurgPointTransformer: Vertebrae Shape Completion with RGB-D Datacited by 0
  27. 2025A Multi-View High-Resolution Foot-Ankle Complex Point Cloud Dataset During Gait for Occlusion-Robust 3D Completioncited by 0
  28. 2025Multi-modal Multi-task Pre-training for Improved Point Cloud Understandingcited by 0
  29. 2025Rethinking the Detail-Preserved Completion of Complex Tubular Structures based on Point Cloud: a Dataset and a Benchmarkcited by 0
  30. 2025The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radarcited by 0
  31. 2025Deep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forestscited by 0
  32. 2025Performance and Efficiency of Climate In-Situ Data Reconstruction: Why Optimized IDW Outperforms kriging and Implicit Neural Representationcited by 0