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xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
Abstract
Unsupervised Domain Adaptation (UDA) is crucial to tackle the lack of annotations in a new domain. There are many multi-modal datasets, but most UDA approaches are uni-modal. In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation. This is challenging as the two input spaces are heterogeneous and can be impacted differently by domain shift. In xMUDA, modalities learn from each other through mutual mimicking, disentangled from the segmentation objective, to prevent the stronger modality from adopting false predictions from the weaker one. We evaluate on new UDA scenarios including day-to-night, country-to-country and dataset-to-dataset, leveraging recent autonomous driving datasets. xMUDA brings large improvements over uni-modal UDA on all tested scenarios, and is complementary to state-of-the-art UDA techniques. Code is available at https://github.com/valeoai/xmuda.
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In the citing paper
“Multiple decoders can also be used but it is a less common choice [121].”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [121]
In the citing paper
“| xMUDA [121] | SparseConvNet3D [125] ResNet34 [16] | 49.1 |”
From Multimodal semantic segmentation in autonomous driving: A review of current approaches and future perspectives (2022) · cites this paper as [121]
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mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Gong, R., Dai, D., Chen, Y., Li, W., & Gool, L. V. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00875 · Cited by 19
Tsai, D., Berrío, J. S., Shan, M., Worrall, S., & Nebot, E. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3185783 · Cited by 18
Cross-Modal Contrastive Learning for Domain Adaptation in 3D Semantic Segmentation
Xing, B., Ying, X., Wang, R., Yang, J., & Chen, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25400 · Cited by 22
Cui, J., Niu, J., He, Y., Liu, D., & Ouyang, Z. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3336753 · Cited by 18
SeCoV2: Semantic Connectivity-Driven Pseudo-Labeling for Robust Cross-Domain Semantic Segmentation
Zhao, D., Zang, Q., Pu, N., Wang, S., Sebe, N., & Zhong, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3596943 · Cited by 20
Wang, Q., Zhang, Y., & He, B. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3247737 · Cited by 18
Source-free Unsupervised Domain Adaptation for 3D Object Detection in Adverse Weather
Hegde, D., Kilic, V., Sindagi, V. A., Cooper, A. B., Foster, M. A., & Patel, V. M. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161341 · Cited by 19
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning
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Li, M., Zhang, Y., Ma, X., Qu, Y., & Fu, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01068 · Cited by 21
Xu, J., Yang, W., Kong, L., Liu, Y., Zhou, Q., Zhang, R., Li, Z., Chen, W., & Fei, B. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3587430 · Cited by 19
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Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
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ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation
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MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
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Traffic thermal infrared texture generation based on siamese semantic CycleGAN
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Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation
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SCoDA: Domain Adaptive Shape Completion for Real Scans
Wu, Y., Yan, Z., Chen, C., Wei, L., Li, X., Li, G., Li, Y., Cui, S. R., & Han, X. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01691 · Cited by 15
OpenForest: a data catalog for machine learning in forest monitoring
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DFAN: Dual-Branch Feature Alignment Network for Domain Adaptation on Point Clouds
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Efficient 3D Scene Semantic Segmentation via Active Learning on Rendered 2D Images
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TPV-IGKD: Image-Guided Knowledge Distillation for 3D Semantic Segmentation With Tri-Plane-View
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RoMo: Robust Unsupervised Multimodal Learning With Noisy Pseudo Labels
Li, Y., Qin, Y., Sun, Y., Peng, D., Peng, X., & Hu, P. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3426482 · Cited by 14
Metadata
- Authors
- Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, E., & Perez, P.
- Affiliations
- Institut national de recherche en sciences et technologies du numérique (FR)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 12602-12611
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035750252
- Cited by
- 224
- References
- 37
- Citations by year
- 2026: 15 · 2025: 35 · 2024: 38 · 2023: 57 · 2022: 42 · 2021: 28 · 2020: 8
- FWCI
- 12.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- unsupervised domain adaptation, cross-modal learning, 3D semantic segmentation, point clouds, 2D images, domain shift, disentangled representation learning, autonomous driving
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