mlsanityData to 14 Oct 2026

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

arXiv 1908.04616 ↗First posted 13 Aug 2019cs.CVcited by 532

Not on the map. Why?

Cited by 22 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentation. Several recent 3D object classification methods have reported state-of-the-art performance on CAD model datasets such as ModelNet40 with high accuracy (~92%). Despite such impressive results, in this paper, we argue that object classification is still a challenging task when objects are framed with real-world settings. To prove this, we introduce ScanObjectNN, a new real-world point cloud object dataset based on scanned indoor scene data. From our comprehensive benchmark, we show that our dataset poses great challenges to existing point cloud classification techniques as objects from real-world scans are often cluttered with background and/or are partial due to occlusions. We identify three key open problems for point cloud object classification, and propose new point cloud classification neural networks that achieve state-of-the-art performance on classifying objects with cluttered background. Our dataset and code are publicly available in our project page https://hkust-vgd.github.io/scanobjectnn/.

Key facts. Cited by 532 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Aug 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 41; 2022: 100; 2023: 116; 2024: 122; 2025: 105; 2026: 48.

532 papers.

Sort:

By area:

  1. 2021E(n) Equivariant Graph Neural Networkscited by 965
  2. 2023Objaverse-XL: A Universe of 10M+ 3D Objectscited by 774
  3. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  4. 2021PointCLIP: Point Cloud Understanding by CLIPcited by 463
  5. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 459
  6. 2022PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategiescited by 436
  7. 2022Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Frameworkcited by 379
  8. 2023OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generationcited by 326
  9. 2022ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understandingcited by 267
  10. 2022Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-trainingcited by 264
  11. 2021A Survey of Embodied AI: From Simulators to Research Taskscited by 261
  12. 2023Uni3D: Exploring Unified 3D Representation at Scalecited by 237
38 more
  1. 2023MVImgNet: A Large-scale Dataset of Multi-view Imagescited by 220
  2. 2023ULIP-2: Towards Scalable Multimodal Pre-training for 3D Understandingcited by 202
  3. 2022CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understandingcited by 198
  4. 2022PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learningcited by 187
  5. 2023OpenShape: Scaling Up 3D Shape Representation Towards Open-World Understandingcited by 184
  6. 2024PointMamba: A Simple State Space Model for Point Cloud Analysiscited by 167
  7. 2023Towards Open Vocabulary Learning: A Surveycited by 166
  8. 2023Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretrainingcited by 164
  9. 2022CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-trainingcited by 160
  10. 2022Masked Discrimination for Self-Supervised Learning on Point Cloudscited by 155
  11. 2021Revisiting Point Cloud Shape Classification with a Simple and Effective Baselinecited by 145
  12. 2022Learning 3D Representations from 2D Pre-trained Models via Image-to-Point Masked Autoencoderscited by 144
  13. 2024ShapeLLM: Universal 3D Object Understanding for Embodied Interactioncited by 137
  14. 2023Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenescited by 129
  15. 2022Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?cited by 122
  16. 2023PointGPT: Auto-regressively Generative Pre-training from Point Cloudscited by 115
  17. 2022CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attentioncited by 103
  18. 2024MeshXL: Neural Coordinate Field for Generative 3D Foundation Modelscited by 103
  19. 2024EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameterscited by 99
  20. 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridgingcited by 99
  21. 2022Surface Representation for Point Cloudscited by 84
  22. 2022Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptionscited by 80
  23. 2023X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoningcited by 80
  24. 2024Move as You Say, Interact as You Can: Language-guided Human Motion Generation with Scene Affordancecited by 80
  25. 2023CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognitioncited by 77
  26. 2021Point Cloud Upsampling via Disentangled Refinementcited by 74
  27. 2022P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Promptingcited by 74
  28. 2022Benchmarking and Analyzing Point Cloud Classification under Corruptionscited by 70
  29. 2021PointCutMix: Regularization Strategy for Point Cloud Classificationcited by 69
  30. 2023CLIP2: Contrastive Language-Image-Point Pretraining from Real-World Point Cloud Datacited by 69
  31. 2022Towards Implicit Text-Guided 3D Shape Generationcited by 68
  32. 2022Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matchingcited by 68
  33. 2023Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Modelscited by 68
  34. 2024MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Modelscited by 67
  35. 2024Point Cloud Mamba: Point Cloud Learning via State Space Modelcited by 66
  36. 2021PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysiscited by 58
  37. 2024Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Modelcited by 57
  38. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 56

50 most cited of 532 All 532 (JSON)