mlsanityData to 7 Oct 2026

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

arXiv 1612.00593 ↗First posted 2 Dec 2016cs.CVcited by 5,223

On the map: World models → World modeling from LiDAR generation

Abstract (arXiv)

Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images. This, however, renders data unnecessarily voluminous and causes issues. In this paper, we design a novel type of neural network that directly consumes point clouds and well respects the permutation invariance of points in the input. Our network, named PointNet, provides a unified architecture for applications ranging from object classification, part segmentation, to scene semantic parsing. Though simple, PointNet is highly efficient and effective. Empirically, it shows strong performance on par or even better than state of the art. Theoretically, we provide analysis towards understanding of what the network has learnt and why the network is robust with respect to input perturbation and corruption.

Key facts. Cited by 5,223 papers in the map (data through 7 Oct 2026). Placed in World modeling from LiDAR generation, one of 11 directions in World models. Builds on 0 of its 32 references that are in the map. First posted on arXiv 2 Dec 2016.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2017: 1; 2018: 0; 2019: 1; 2020: 2; 2021: 822; 2022: 1007; 2023: 1016; 2024: 943; 2025: 891; 2026: 540.

5,223 papers.

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  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  2. 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,526
  3. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  4. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  5. 2022Diffusion Models in Vision: A Surveycited by 1,008
  6. 2023ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenescited by 747
  7. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 734
  8. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 698
  9. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 647
  10. 2023Point Transformer V3: Simpler, Faster, Strongercited by 579
  11. 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
  12. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
38 more
  1. 2021Neural Fields in Visual Computing and Beyondcited by 539
  2. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  3. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  4. 2023Ferret: Refer and Ground Anything Anywhere at Any Granularitycited by 498
  5. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 482
  6. 2022TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformerscited by 464
  7. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 461
  8. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  9. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  10. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 432
  11. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 384
  12. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 376
  13. 2023Splatter Image: Ultra-Fast Single-View 3D Reconstructioncited by 359
  14. 2022Tip-Adapter: Training-free Adaption of CLIP for Few-shot Classificationcited by 355
  15. 2022BEVFusion: A Simple and Robust LiDAR-Camera Fusion Frameworkcited by 334
  16. 2021An End-to-End Transformer Model for 3D Object Detectioncited by 329
  17. 2023PointLLM: Empowering Large Language Models to Understand Point Cloudscited by 326
  18. 2023Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Drivingcited by 325
  19. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 324
  20. 2023ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skillscited by 313
  21. 2022ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulationcited by 306
  22. 2023VoxFormer: Sparse Voxel Transformer for Camera-based 3D Semantic Scene Completioncited by 306
  23. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  24. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
  25. 2022Mask3D: Mask Transformer for 3D Semantic Instance Segmentationcited by 299
  26. 2024Transolver: A Fast Transformer Solver for PDEs on General Geometriescited by 299
  27. 2023OpenMask3D: Open-Vocabulary 3D Instance Segmentationcited by 288
  28. 2023A Survey on Multimodal Large Language Models for Autonomous Drivingcited by 288
  29. 2021PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformerscited by 284
  30. 2021HDMapNet: An Online HD Map Construction and Evaluation Frameworkcited by 280
  31. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  32. 2022Motion Transformer with Global Intention Localization and Local Movement Refinementcited by 267
  33. 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
  34. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 263
  35. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 262
  36. 2023Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformerscited by 257
  37. 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
  38. 2021Voxel Transformer for 3D Object Detectioncited by 247

50 most cited of 5,223 All 5,223 (JSON)

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  1. 2025Point-MoE: Large-Scale Multi-Dataset Training with Mixture-of-Experts for 3D Semantic Segmentation
  2. 2025Point Linguist Model: Segment Any Object via Bridged Large 3D-Language Model
  3. 20253D-PointZshotS: Geometry-Aware 3D Point Cloud Zero-Shot Semantic Segmentation Narrowing the Visual-Semantic Gap
  4. 2026Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds
  5. 2025Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation
  6. 2025PDT: Point Distribution Transformation with Diffusion Models
  7. 2026Learning Point Cloud Geometry as a Statistical Manifold: Theory and Practice ↗
  8. 2018Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks