mlsanityData to 7 Oct 2026

An Empirical Study of Training State-of-the-Art LiDAR Segmentation Models

arXiv 2405.14870 ↗First posted 23 May 2024cs.CV cs.ROcited by 12

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Abstract (arXiv)

In the rapidly evolving field of autonomous driving, precise segmentation of LiDAR data is crucial for understanding complex 3D environments. Traditional approaches often rely on disparate, standalone codebases, hindering unified advancements and fair benchmarking across models. To address these challenges, we introduce MMDetection3D-lidarseg, a comprehensive toolbox designed for the efficient training and evaluation of state-of-the-art LiDAR segmentation models. We support a wide range of segmentation models and integrate advanced data augmentation techniques to enhance robustness and generalization. Additionally, the toolbox provides support for multiple leading sparse convolution backends, optimizing computational efficiency and performance. By fostering a unified framework, MMDetection3D-lidarseg streamlines development and benchmarking, setting new standards for research and application. Our extensive benchmark experiments on widely-used datasets demonstrate the effectiveness of the toolbox. The codebase and trained models have been publicly available, promoting further research and innovation in the field of LiDAR segmentation for autonomous driving.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 69 of its 103 references that are in the map. First posted on arXiv 23 May 2024.

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69 of 103 references.

References · 69

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  9. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
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38 more
  1. 2021(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Networkcited by 164
  2. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 157
  3. 2019xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentationcited by 129
  4. 2023Robo3D: Towards Robust and Reliable 3D Perception against Corruptionscited by 128
  5. 2023Rethinking Range View Representation for LiDAR Segmentationcited by 126
  6. 2023Segment Any Point Cloud Sequences by Distilling Vision Foundation Modelscited by 123
  7. 2022Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentationcited by 118
  8. 2021Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weathercited by 95
  9. 2022LaserMix for Semi-Supervised LiDAR Semantic Segmentationcited by 92
  10. 2020AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentationcited by 87
  11. 2020Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challengescited by 80
  12. 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
  13. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  14. 2023RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Drivingcited by 76
  15. 2022PolarMix: A General Data Augmentation Technique for LiDAR Point Cloudscited by 74
  16. 2022TorchSparse: Efficient Point Cloud Inference Enginecited by 72
  17. 2022Scribble-Supervised LiDAR Semantic Segmentationcited by 69
  18. 2022ALSO: Automotive Lidar Self-supervision by Occupancy estimationcited by 68
  19. 2023MSeg3D: Multi-modal 3D Semantic Segmentation for Autonomous Drivingcited by 68
  20. 2020DALES: A Large-scale Aerial LiDAR Data Set for Semantic Segmentationcited by 65
  21. 20214D Panoptic LiDAR Segmentationcited by 64
  22. 2021Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Datacited by 64
  23. 2023UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebasecited by 57
  24. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  25. 2022Unsupervised Point Cloud Representation Learning with Deep Neural Networks: A Surveycited by 54
  26. 2023Towards Label-free Scene Understanding by Vision Foundation Modelscited by 54
  27. 2023Using a Waffle Iron for Automotive Point Cloud Semantic Segmentationcited by 53
  28. 2024Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Drivingcited by 53
  29. 2023FRNet: Frustum-Range Networks for Scalable LiDAR Segmentationcited by 50
  30. 2022Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detectioncited by 49
  31. 2023RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptionscited by 47
  32. 2024Better Call SAL: Towards Learning to Segment Anything in Lidarcited by 47
  33. 2020Panoster: End-to-end Panoptic Segmentation of LiDAR Point Cloudscited by 46
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  35. 2021FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decodingcited by 46
  36. 2023Three Pillars improving Vision Foundation Model Distillation for Lidarcited by 43
  37. 2023RoboBEV: Towards Robust Bird's Eye View Perception under Corruptionscited by 42
  38. 2023PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awarenesscited by 42

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Cited by year

Citing papers per year: 2024: 4; 2025: 8.

12 papers.

  1. 2024Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Drivingcited by 39
  2. 20244D Contrastive Superflows are Dense 3D Representation Learnerscited by 32
  3. 2024RAPiD-Seg: Range-Aware Pointwise Distance Distribution Networks for 3D LiDAR Segmentationcited by 15
  4. 2025LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous Drivingcited by 11
  5. 2025SPIRAL: Semantic-Aware Progressive LiDAR Scene Generation and Understandingcited by 10
  6. 2025Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representationscited by 7
  7. 2025U4D: Uncertainty-Aware 4D World Modeling from LiDAR Sequencescited by 5
  8. 2025Enhanced Spatiotemporal Consistency for Image-to-LiDAR Data Pretrainingcited by 4
  9. 2024PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAMcited by 3
  10. 2025An Experimental Study of SOTA LiDAR Segmentation Modelscited by 0
  11. 2025Depth-Aware Range Image-Based Model for Point Cloud Segmentationcited by 0
  12. 2025UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehiclescited by 0