mlsanityData to 7 Oct 2026

Sim2real Transfer Learning for Point Cloud Segmentation: An Industrial Application Case on Autonomous Disassembly

arXiv 2301.05033 ↗First posted 12 Jan 2023cs.CVcited by 16

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Abstract (arXiv)

On robotics computer vision tasks, generating and annotating large amounts of data from real-world for the use of deep learning-based approaches is often difficult or even impossible. A common strategy for solving this problem is to apply simulation-to-reality (sim2real) approaches with the help of simulated scenes. While the majority of current robotics vision sim2real work focuses on image data, we present an industrial application case that uses sim2real transfer learning for point cloud data. We provide insights on how to generate and process synthetic point cloud data in order to achieve better performance when the learned model is transferred to real-world data. The issue of imbalanced learning is investigated using multiple strategies. A novel patch-based attention network is proposed additionally to tackle this problem.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 34 of its 59 references that are in the map. First posted on arXiv 12 Jan 2023.

Builds on

34 of 59 references.

References · 34

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  4. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  6. 2020Point Transformercited by 1,462
  7. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
  8. 2019KPConv: Flexible and Deformable Convolution for Point Cloudscited by 1,202
  9. 2019A Comprehensive Survey on Transfer Learningcited by 1,154
  10. 2020PCT: Point cloud transformercited by 730
  11. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  12. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 656
22 more
  1. 2019RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Cloudscited by 584
  2. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
  3. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 388
  4. 2017Learning from Synthetic Humanscited by 358
  5. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  6. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  7. 2019Domain Randomization and Pyramid Consistency: Simulation-to-Real Generalization without Accessing Target Domain Datacited by 245
  8. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 182
  9. 2018Tangent Convolutions for Dense Prediction in 3Dcited by 173
  10. 2021Deep Parametric Continuous Convolutional Neural Networkscited by 161
  11. 2017Pointwise Convolutional Neural Networkscited by 126
  12. 2018Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimationcited by 92
  13. 2017Deep Projective 3D Semantic Segmentationcited by 81
  14. 2019BlenderProccited by 76
  15. 2018PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentationcited by 58
  16. 2019Meta Reinforcement Learning for Sim-to-real Domain Adaptationcited by 49
  17. 2021Auto-Tuned Sim-to-Real Transfercited by 45
  18. 2020EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenescited by 29
  19. 2019SynthCity: A large scale synthetic point cloudcited by 26
  20. 2016Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networkscited by 25
  21. 2019Learning to Augment Synthetic Images for Sim2Real Policy Transfercited by 16
  22. 2019GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloudcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 7; 2026: 2.

16 papers.

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  1. 2024Flow as the Cross-Domain Manipulation Interfacecited by 126
  2. 2024Neural MP: A Generalist Neural Motion Plannercited by 18
  3. 2024Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Surveycited by 9
  4. 2024Advancements in Point Cloud-Based 3D Defect Detection and Classification for Industrial Systems: A Comprehensive Surveycited by 3
  5. 2024Rethinking Attention Module Design for Point Cloud Analysiscited by 2
  6. 2025JiSAM: Alleviate Labeling Burden and Corner Case Problems in Autonomous Driving via Minimal Real-World Datacited by 2
  7. 2025TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policiescited by 2
  8. 2025TrueCity: Real and Simulated Urban Data for Cross-Domain 3D Scene Understandingcited by 2
  9. 2025SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformitycited by 1
  10. 2025Mind the Domain Gap: Measuring the Domain Gap Between Real-World and Synthetic Point Clouds for Automated Driving Developmentcited by 1
  11. 20233D-TexSeg: Unsupervised Segmentation of 3D Texture using Mutual Transformer Learningcited by 0
  12. 2024SuDA: Support-based Domain Adaptation for Sim2Real Motion Capture with Flexible Sensorscited by 0
4 more
  1. 20256D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassemblycited by 0
  2. 2025Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environmentscited by 0
  3. 2026Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraintscited by 0
  4. 2026Open-vocabulary 3D scene perception in industrial environmentscited by 0