mlsanityData to 7 Oct 2026

ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perception

arXiv 2310.16542 ↗First posted 25 Oct 2023cs.CV cs.ROcited by 5

Not on the map. Why?

Abstract (arXiv)

LiDAR is an essential sensor for autonomous driving by collecting precise geometric information regarding a scene. %Exploiting this information for perception is interesting as the amount of available data increases. As the performance of various LiDAR perception tasks has improved, generalizations to new environments and sensors has emerged to test these optimized models in real-world conditions. This paper provides a novel dataset, ParisLuco3D, specifically designed for cross-domain evaluation to make it easier to evaluate the performance utilizing various source datasets. Alongside the dataset, online benchmarks for LiDAR semantic segmentation, LiDAR object detection, and LiDAR tracking are provided to ensure a fair comparison across methods. The ParisLuco3D dataset, evaluation scripts, and links to benchmarks can be found at the following website:https://npm3d.fr/parisluco3d

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 36 of its 47 references that are in the map. First posted on arXiv 25 Oct 2023.

Builds on

36 of 47 references.

References · 36

  1. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
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24 more
  1. 2020Searching Efficient 3D Architectures with Sparse Point-Voxel Convolutioncited by 422
  2. 2020Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentationcited by 388
  3. 2018Two at Once: Enhancing Learning and Generalization Capacities via IBN-Netcited by 361
  4. 2020PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentationcited by 274
  5. 2018SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloudcited by 228
  6. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 221
  7. 2021One Million Scenes for Autonomous Driving: ONCE Datasetcited by 218
  8. 2021PandaSet: Advanced Sensor Suite Dataset for Autonomous Drivingcited by 186
  9. 2020Train in Germany, Test in The USA: Making 3D Object Detectors Generalizecited by 150
  10. 2020SemanticPOSS: A Point Cloud Dataset with Large Quantity of Dynamic Instancescited by 123
  11. 2023DSVT: Dynamic Sparse Voxel Transformer with Rotated Setscited by 122
  12. 2020Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Cloudscited by 116
  13. 2022CenterFormer: Center-based Transformer for 3D Object Detectioncited by 99
  14. 2022CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Drivingcited by 56
  15. 2022CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentationcited by 54
  16. 20213D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detectioncited by 43
  17. 2021CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closurecited by 38
  18. 2022Domain generalization of 3D semantic segmentation in autonomous drivingcited by 25
  19. 2022Online Segmentation of LiDAR Sequences: Dataset and Algorithmcited by 14
  20. 2023MDT3D: Multi-Dataset Training for LiDAR 3D Object Detection Generalizationcited by 14
  21. 2022OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Datacited by 13
  22. 2023Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentationcited by 13
  23. 2023Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehiclescited by 9
  24. 2022COLA: COarse LAbel pre-training for 3D semantic segmentation of sparse LiDAR datasetscited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 0; 2025: 3.

5 papers.

  1. 2023WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environmentscited by 16
  2. 2023COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous drivingcited by 8
  3. 2025Synth It Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarioscited by 3
  4. 2025DOS: Distilling Observable Softmaps of Zipfian Prototypes for Self-Supervised Point Representationcited by 2
  5. 2025Semi-Supervised Diversity-Aware Domain Adaptation for 3D Object detectioncited by 0