# Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions

arXiv: https://arxiv.org/abs/2010.12239 · HTML: https://mlsanity.com/paper/2010.12239/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2020.

## Built on by (most cited first, 17 of 17)

- [Learning 3D Semantic Segmentation with only 2D Image Supervision](https://mlsanity.com/paper/2110.11325/) (2021)
- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation](https://mlsanity.com/paper/2111.15242/) (2021)
- [Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters](https://mlsanity.com/paper/2107.09783/) (2021)
- [SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation](https://mlsanity.com/paper/2304.03251/) (2023)
- [Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation](https://mlsanity.com/paper/2109.15286/) (2021)
- [See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation](https://mlsanity.com/paper/2111.09450/) (2021)
- [Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation](https://mlsanity.com/paper/2304.02991/) (2023)
- [LiDAR-CS Dataset: LiDAR Point Cloud Dataset with Cross-Sensors for 3D Object Detection](https://mlsanity.com/paper/2301.12515/) (2023)
- [TALoS: Enhancing Semantic Scene Completion via Test-time Adaptation on the Line of Sight](https://mlsanity.com/paper/2410.15674/) (2024)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation](https://mlsanity.com/paper/2210.09739/) (2022)
- [Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors](https://mlsanity.com/paper/2212.09517/) (2022)
- [Active Classification of Moving Targets with Learned Control Policies](https://mlsanity.com/paper/2212.03068/) (2022)
- [Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2108.06608/) (2021)
- [Domain Adaptation in LiDAR Semantic Segmentation via Alternating Skip Connections and Hybrid Learning](https://mlsanity.com/paper/2201.05585/) (2022)
- [Simulation-to-Reality domain adaptation for offline 3D object annotation on pointclouds with correlation alignment](https://mlsanity.com/paper/2202.02666/) (2022)

## Abstract (arXiv)

LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or adapt to additional domains, where data distribution is different, remains a major challenge. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we propose a learning-based approach that aligns the distribution of the semantic classes of the target domain to the source domain. The presented ablation study shows how each part contributes to the final performance. Our strategy is shown to outperform previous approaches for domain adaptation with comparisons run on three different domains.