mlsanityData to 7 Oct 2026

Sequential Point Clouds: A Survey

arXiv 2204.09337 ↗First posted 20 Apr 2022cs.CVcited by 5

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Abstract (arXiv)

Point cloud has drawn more and more research attention as well as real-world applications. However, many of these applications (e.g. autonomous driving and robotic manipulation) are actually based on sequential point clouds (i.e. four dimensions) because the information of the static point cloud data could provide is still limited. Recently, researchers put more and more effort into sequential point clouds. This paper presents an extensive review of the deep learning-based methods for sequential point cloud research including dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Finally, this paper is concluded by discussing the challenges in the current sequential point cloud research and pointing out insightful potential future research directions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 104 of its 160 references that are in the map. First posted on arXiv 20 Apr 2022.

Builds on

104 of 160 references.

References · 104

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  4. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  5. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  6. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  7. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  8. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  9. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
  10. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  11. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  12. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
38 more
  1. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  2. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  3. 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
  4. 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 972
  5. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 966
  6. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  7. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 878
  8. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  9. 2018Panoptic Segmentationcited by 796
  10. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 780
  11. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  12. 20173D Semantic Segmentation with Submanifold Sparse Convolutional Networkscited by 746
  13. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
  14. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  15. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  16. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  17. 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
  18. 2018Hypergraph Neural Networkscited by 482
  19. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  20. 2020Local Implicit Grid Representations for 3D Scenescited by 372
  21. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  22. 2016OctNet: Learning Deep 3D Representations at High Resolutionscited by 340
  23. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  24. 2018FlowNet3D: Learning Scene Flow in 3D Point Cloudscited by 241
  25. 2021End-to-end Interpretable Neural Motion Plannercited by 240
  26. 2017SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloudcited by 231
  27. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  28. 2020Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Netcited by 228
  29. 2019MOTS: Multi-Object Tracking and Segmentationcited by 222
  30. 2019Disentangling Monocular 3D Object Detectioncited by 215
  31. 2017SEGCloud: Semantic Segmentation of 3D Point Cloudscited by 178
  32. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  33. 2019End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Cloudscited by 171
  34. 2020SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentationcited by 170
  35. 2018SPLATNet: Sparse Lattice Networks for Point Cloud Processingcited by 158
  36. 2021IntentNet: Learning to Predict Intention from Raw Sensor Datacited by 158
  37. 2019Modeling Point Clouds with Self-Attention and Gumbel Subset Samplingcited by 151
  38. 2021Offboard 3D Object Detection from Point Cloud Sequencescited by 144

50 most cited of 104 All 104 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025TraF-Align: Trajectory-aware Feature Alignment for Asynchronous Multi-agent Perceptioncited by 15
  2. 2025M^3Detection: Multi-Frame Multi-Level Feature Fusion for Multi-Modal 3D Object Detection with Camera and 4D Imaging Radarcited by 1
  3. 2024A Preprocessing and Postprocessing Voxel-based Method for LiDAR Semantic Segmentation Improvement in Long Distancecited by 0
  4. 2025The Impact of 2D Segmentation Backbones on Point Cloud Predictions Using 4D Radarcited by 0
  5. 2026PiVoT: A Variational Solution for Real-time Large-scale Multi-object Detection and Tracking under Heavy Cluttercited by 0