mlsanityData to 7 Oct 2026

3D objects and scenes classification, recognition, segmentation, and reconstruction using 3D point cloud data: A review

arXiv 2306.05978 ↗First posted 9 Jun 2023cs.CVcited by 2

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Abstract (arXiv)

Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studies

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 46 of its 115 references that are in the map. First posted on arXiv 9 Jun 2023.

Builds on

46 of 115 references.

References · 46

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  6. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  7. 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
  8. 2018Pixel2Mesh: Generating 3D Mesh Models from Single RGB Imagescited by 649
  9. 2019Relation-Shape Convolutional Neural Network for Point Cloud Analysiscited by 346
  10. 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
  11. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  12. 2018PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognitioncited by 197
34 more
  1. 2019DeepHuman: 3D Human Reconstruction from a Single Imagecited by 182
  2. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  3. 2018The Perfect Match: 3D Point Cloud Matching with Smoothed Densitiescited by 169
  4. 2019PU-GAN: a Point Cloud Upsampling Adversarial Networkcited by 162
  5. 2018Scan2CAD: Learning CAD Model Alignment in RGB-D Scanscited by 142
  6. 2019MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequencescited by 117
  7. 2019Interpolated Convolutional Networks for 3D Point Cloud Understandingcited by 86
  8. 2020UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Treecited by 77
  9. 2020Weakly Supervised 3D Object Detection from Lidar Point Cloudcited by 67
  10. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  11. 20163D Fully Convolutional Network for Vehicle Detection in Point Cloudcited by 57
  12. 2021EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detectioncited by 49
  13. 2019Transductive Zero-Shot Learning for 3D Point Cloud Classificationcited by 39
  14. 2018PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognitioncited by 36
  15. 2020RfD-Net: Point Scene Understanding by Semantic Instance Reconstructioncited by 36
  16. 2019Zero-shot Learning of 3D Point Cloud Objectscited by 34
  17. 2022DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detectioncited by 25
  18. 2021Panoptic Segmentation: A Reviewcited by 18
  19. 2016Orientation-boosted Voxel Nets for 3D Object Recognitioncited by 17
  20. 2021BADet: Boundary-Aware 3D Object Detection from Point Cloudscited by 17
  21. 2020PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognitioncited by 16
  22. 2022Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approachescited by 15
  23. 2021HyperPocket: Generative Point Cloud Completioncited by 12
  24. 2021SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloudcited by 12
  25. 2016Density-based Denoising of Point Cloudcited by 11
  26. 2020HyperFlow: Representing 3D Objects as Surfacescited by 7
  27. 2017Rotational Subgroup Voting and Pose Clustering for Robust 3D Object Recognitioncited by 6
  28. 2017Iterative Closest Labeled Point for Tactile Object Shape Recognitioncited by 4
  29. 2020Investigating the Importance of Shape Features, Color Constancy, Color Spaces and Similarity Measures in Open-Ended 3D Object Recognitioncited by 4
  30. 2015PERCH: Perception via Search for Multi-Object Recognition and Localizationcited by 3
  31. 2019Silhouette Guided Point Cloud Reconstruction beyond Occlusioncited by 3
  32. 2015Building with Drones: Accurate 3D Facade Reconstruction using MAVscited by 2
  33. 2022R2-MLP: Round-Roll MLP for Multi-View 3D Object Recognitioncited by 2
  34. 2020Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Drivingcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024FADet: A Multi-sensor 3D Object Detection Network based on Local Featured Attentioncited by 1
  2. 2026Leveraging a Statistical Shape Model for Efficient Generation of Annotated Training Data: A Case Study on Liver Landmarks Segmentationcited by 0