The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
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]
Citations
Cites
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
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Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpacePreprint
Qi, C. R., Yi, L., Su, H., & Guibas, L. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02413 · Cited by 10,099
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., & Solomon, J. M. (2019) · ACM Transactions on Graphics · doi:10.1145/3326362 · Cited by 7,199
nuScenes: A Multimodal Dataset for Autonomous Driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., & Beijbom, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01164 · Cited by 7,152
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T., & Niessner, M. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.261 · Cited by 4,368
KPConv: Flexible and Deformable Convolution for Point Clouds
Thomas, H., Qi, C. R., Deschaud, J., Marcotegui, B., Goulette, F., & Guibas, L. J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00651 · Cited by 3,400
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., & Darrell, T. (2017) · arXiv · doi:10.48550/arxiv.1711.03213 · Cited by 2,656
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- lee, 2013, Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks, ICML Workshop
- jakob, 2019, A2D2 AEV Autonomous Driving Dataset
Cited by
Sorted by citations
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., & Tomizuka, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_1 · Cited by 448
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
Yan, X., Gao, J., Zheng, C., Zheng, C., Zhang, R., Cui, S. R., & Li, Z. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19815-1_39 · Cited by 301
Perception-Aware Multi-Sensor Fusion for 3D LiDAR Semantic Segmentation
Zhuang, Z., Li, R., Jia, K., Wang, Q., Li, Y., & Tan, M. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01597 · Cited by 216
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
Yang, J., Shi, S., Wang, Z., Li, H., & Qi, X. (2021) · 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/cvpr46437.2021.01023 · Cited by 212
Self-Supervised Learning for Domain Adaptation on Point Clouds
Achituve, I., Maron, H., & Chechik, G. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00017 · Cited by 175
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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