mlsanityData to 7 Oct 2026

Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation

arXiv 2304.11705 ↗First posted 23 Apr 2023cs.CV cs.AI cs.LGcited by 13

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Abstract (arXiv)

The ability to deploy robots that can operate safely in diverse environments is crucial for developing embodied intelligent agents. As a community, we have made tremendous progress in within-domain LiDAR semantic segmentation. However, do these methods generalize across domains? To answer this question, we design the first experimental setup for studying domain generalization (DG) for LiDAR semantic segmentation (DG-LSS). Our results confirm a significant gap between methods, evaluated in a cross-domain setting: for example, a model trained on the source dataset (SemanticKITTI) obtains 26.53 mIoU on the target data, compared to 48.49 mIoU obtained by the model trained on the target domain (nuScenes). To tackle this gap, we propose the first method specifically designed for DG-LSS, which obtains 34.88 mIoU on the target domain, outperforming all baselines. Our method augments a sparse-convolutional encoder-decoder 3D segmentation network with an additional, dense 2D convolutional decoder that learns to classify a birds-eye view of the point cloud. This simple auxiliary task encourages the 3D network to learn features that are robust to sensor placement shifts and resolution, and are transferable across domains. With this work, we aim to inspire the community to develop and evaluate future models in such cross-domain conditions.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 56 of its 69 references that are in the map. First posted on arXiv 23 Apr 2023.

Builds on

56 of 69 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 3; 2025: 3; 2026: 1.

13 papers.

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  1. 2023Three Pillars improving Vision Foundation Model Distillation for Lidarcited by 43
  2. 2024UniMix: Towards Domain Adaptive and Generalizable LiDAR Semantic Segmentation in Adverse Weathercited by 24
  3. 2023WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environmentscited by 16
  4. 2023Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentationcited by 11
  5. 2023COLA: COarse-LAbel multi-source LiDAR semantic segmentation for autonomous drivingcited by 8
  6. 2023Rethinking LiDAR Domain Generalization: Single Source as Multiple Density Domainscited by 7
  7. 2024HGL: Hierarchical Geometry Learning for Test-time Adaptation in 3D Point Cloud Segmentationcited by 6
  8. 2025AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Constructioncited by 6
  9. 2023ParisLuco3D: A high-quality target dataset for domain generalization of LiDAR perceptioncited by 5
  10. 2024Subspace Prototype Guidance for Mitigating Class Imbalance in Point Cloud Semantic Segmentationcited by 4
  11. 2025Improving Multimodal Distillation for 3D Semantic Segmentation under Domain Shiftcited by 2
  12. 2025UniFlow: Zero-Shot LiDAR Scene Flow for Autonomous Vehiclescited by 0
1 more
  1. 2026Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentationcited by 0