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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Multiple decoders can also be used but it is a less common choice.
“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]
xMUDA, using SparseConvNet3D and ResNet34 backbones, achieves an mIoU of 49.1 on the KITTI dataset for 2D + 3D semantic segmentation.
“| 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]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
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 · see citing works
Are we ready for autonomous driving? The KITTI vision benchmark suite
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 · see citing works
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204 · see citing works
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 9,834 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Frustum PointNets for 3D Object Detection from RGB-D Data
Qi, C. R., Liu, W., Wu, C., Su, H., & Guibas, L. J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00102 · Cited by 2,655 · see citing works
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00939 · Cited by 2,294 · see citing works
4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
Choy, C., Gwak, J., & Savarese, S. (2019) · 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/cvpr.2019.00319 · Cited by 2,067 · see citing works
Zhang, Y., Xiang, T., Hospedales, T. M., & Lu, H. (2018) · 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/cvpr.2018.00454 · Cited by 1,933 · see citing works
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
Graham, B., Engelcke, M., & Maaten, L. V. D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00961 · Cited by 1,788 · see citing works
Learning to Adapt Structured Output Space for Semantic Segmentation
Tsai, Y., Hung, W.-C., Schulter, S., Sohn, K., Yang, M., & Chandraker, M. K. (2018) · 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/cvpr.2018.00780 · Cited by 1,753 · see citing works
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., & Perez, P. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00262 · Cited by 1,524 · see citing works
Self-citationDeep Continuous Fusion for Multi-sensor 3D Object Detection
Liang, M., Yang, B., Wang, S., & Urtasun, R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01270-0_39 · Cited by 993 · see citing works
Wu, B., Wan, A., Yue, X., & Keutzer, K. (2018) · 2018 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2018.8462926 · Cited by 967 · see citing works
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
Hazirbas, C., Ma, L., Domokos, C., & Cremers, D. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-54181-5_14 · Cited by 902 · see citing works
Confidence Regularized Self-Training
Zou, Y., Yu, Z., Liu, X., Kumar, B. V. K. V., & Wang, J. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00608 · Cited by 863 · see citing works
SPLATNet: Sparse Lattice Networks for Point Cloud Processing
Su, H., Jampani, V., Sun, D., Maji, S., Kalogerakis, E., Yang, M., & Kautz, J. (2018) · 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/cvpr.2018.00268 · Cited by 804 · see citing works
Wu, B., Zhou, X., Zhao, S., Yue, X., & Keutzer, K. (2019) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2019.8793495 · Cited by 761 · see citing works
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Liang, M., Yang, B., Chen, Y., Hu, R., & Urtasun, R. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00752 · Cited by 723 · see citing works
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
Li, Y., Yuan, L., & Vasconcelos, N. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00710 · Cited by 704 · see citing works
FCNs in the Wild: Pixel-level Adversarial and Constraint-based AdaptationPreprint
Hoffman, J., Wang, D., Yu, F., & Darrell, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.02649 · Cited by 692 · see citing works
ScanNet: Richly-annotated 3D Reconstructions of Indoor ScenesPreprint
Dai, A., Chang, A. X., Savva, M., Halber, M., Funkhouser, T. A., & Nießner, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.04405 · Cited by 537 · see citing works
Deep Parametric Continuous Convolutional Neural Networks
Wang, S., Suo, S., Ma, W.-C., Pokrovsky, A., & Urtasun, R. (2018) · 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/cvpr.2018.00274 · Cited by 470 · see citing works
Deep Continuous Fusion for Multi-Sensor 3D Object DetectionPreprint
Liang, M. X., Yang, B., Wang, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.10992 · Cited by 428 · see citing works
Self-supervised model adaptation for multimodal semantic segmentation
Valada, A., Mohan, R., & Burgard, W. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-019-01188-y · Cited by 293 · see citing works
Dynamic Graph CNN for Learning on Point CloudsPreprint
Wang, Y., Sun, Y., Liu, Z., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.07829 · Cited by 287 · see citing works
DADA: Depth-Aware Domain Adaptation in Semantic Segmentation
Vu, T.-H., Jain, H., Bucher, M., Cord, M., & Pérez, P. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00746 · Cited by 190 · see citing works
Self-citationMulti-View PointNet for 3D Scene Understanding
Jaritz, M., Gu, J., & Su, H. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00494 · Cited by 169 · see citing works
Self-citationSensor Fusion for Joint 3D Object Detection and Semantic Segmentation
Meyer, G. P., Charland, J., Hegde, D. K., Laddha, A., & Vallespi-Gonzalez, C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00162 · Cited by 158 · see citing works
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain AdaptationPreprint
Morerio, P., Cavazza, J., & Murino, V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10288 · Cited by 112 · see citing works
SPIGAN: Privileged Adversarial Learning from SimulationPreprint
Lee, K.-H., Ros, G., Li, J., & Gaidon, A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.03756 · Cited by 80 · see citing works
A Unified Point-Based Framework for 3D Segmentation
Chiang, H.-Y., Lin, Y.-L., Liu, Y.-C., & Hsu, W. H. (2019) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2019.00026 · Cited by 65 · see citing works
Flagged first, then by citations
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 436 · see citing works
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 285 · see citing works
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 · see citing works
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 201 · see citing works
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 · see citing works
The Norm Must Go On: Dynamic Unsupervised Domain Adaptation by Normalization
Mirza, M. J., Micorek, J., Possegger, H., & Bischof, H. (2021) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.01435 · Cited by 205 · see citing works
Bidirectional Projection Network for Cross Dimension Scene Understanding
Hu, W., Zhao, H., Jiang, L., Jia, J., & Wong, T. (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/cvpr46437.2021.01414 · Cited by 118 · see citing works
Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data
Sautier, C., Puy, G., Gidaris, S., Boulch, A., Bursuc, A., & Marlet, R. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00966 · Cited by 188 · see citing works
Scene Graph Generation: A comprehensive survey
Li, H., Zhu, G., Zhang, L., Jiang, Y., Dang, Y., Hou, H., Shen, P., Zhao, X., Shah, S. A. A., & Bennamoun, M. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.127052 · Cited by 114 · see citing works
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
Kong, L., Liu, Y., Li, X. S., Chen, R., Zhang, W., Ren, J., Pan, L., Chen, K., & Liu, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01830 · Cited by 108 · see citing works
Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey of Datasets and Methods
Gao, B., Pan, Y., Li, C., Geng, S., & Zhao, H. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3076844 · Cited by 105 · see citing works
Zhang, P., Xu, H., Tian, T., Gao, P., Li, L., Zhao, T., Zhang, N., & Tian, J. (2022) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2022.3169339 · Cited by 89 · see citing works
Unsupervised Domain Adaptive 3D Detection with Multi-Level Consistency
Luo, Z., Cai, Z., Zhou, C., Zhang, G., Zhao, H., Yi, S., Lu, S., Li, H., Zhang, S., & Liu, Z. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00874 · Cited by 83 · see citing works
Domain Transfer for Semantic Segmentation of LiDAR Data using Deep Neural Networks
Langer, F., Milioto, A., Haag, A., Behley, J., & Stachniss, C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341508 · Cited by 76 · see citing works
Learning 3D Semantic Segmentation with only 2D Image Supervision
Genova, K., Yin, X., Kundu, A., Pantofaru, C., Cole, F. H., Sud, A., Brewington, B. E., Shucker, B., & Funkhouser, T. A. (2021) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv53792.2021.00046 · Cited by 76 · see citing works
A Survey of Label-Efficient Deep Learning for 3D Point Clouds
Xiao, A., Zhang, X., Shao, L., & Lu, S. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3416302 · Cited by 76 · see citing works
Multi-Modal Cross-Domain Alignment Network for Video Moment Retrieval
Fang, X., Liu, D., Zhou, P., & Hu, Y. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3222965 · Cited by 74 · see citing works
Cross-Modal Learning for Domain Adaptation in 3D Semantic Segmentation
Jaritz, M., Vu, T.-H., de Charette, R., Wirbel, É., & Pérez, P. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3159589 · Cited by 75 · see citing works
Self-citationAdvancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
Sohail, S. S., Himeur, Y., Kheddar, H., Amira, A., Fadli, F., Atalla, S., Copiaco, A., & Mansoor, W. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102601 · Cited by 75 · see citing works
Peng, D., Lei, Y., Li, W., Zhang, P., & Guo, Y. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00702 · Cited by 68 · see citing works
LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection
Wei, Y., Wei, Z., Rao, Y., Li, J., Zhou, J. N., & Lu, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19842-7_11 · Cited by 67 · see citing works
A Survey on Deep Domain Adaptation for LiDAR Perception
Triess, L. T., Dreißig, M., Rist, C. B., & Zöllner, J. M. (2021) · IEEE Intelligent Vehicles Symposium Workshops (IV Workshops) · doi:10.1109/ivworkshops54471.2021.9669228 · Cited by 62 · see citing works
MM-TTA: Multi-Modal Test-Time Adaptation for 3D Semantic Segmentation
Shin, I., Tsai, Y., Zhuang, B., Schulter, S., Liu, B., Garg, S., Kweon, I. S., & Yoon, K. (2022) · 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/cvpr52688.2022.01642 · Cited by 63 · see citing works
Fan, H., Chang, X.-J., Zhang, W., Cheng, Y., Sun, Y., & Kankanhalli, M. (2022) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52688.2022.00627 · Cited by 64 · see citing works
CoSMix: Compositional Semantic Mix for Domain Adaptation in 3D LiDAR Segmentation
Saltori, C., Galasso, F., Fiameni, G., Sebe, N., Ricci, E., & Poiesi, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_34 · Cited by 62 · see citing works
MS-UDA: Multi-Spectral Unsupervised Domain Adaptation for Thermal Image Semantic Segmentation
Kim, Y.-H., Shin, U., Park, J., & Kweon, I. S. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3093652 · Cited by 60 · see citing works
LiDARNet: A Boundary-Aware Domain Adaptation Model for Point Cloud Semantic Segmentation
Jiang, P., & Saripalli, S. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561255 · Cited by 67 · see citing works
ST3D++: Denoised Self-Training for Unsupervised Domain Adaptation on 3D Object Detection
Yang, J., Shi, S., Wang, Z., Li, H., & Qi, X. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3216606 · Cited by 60 · see citing works
Rizzoli, G., Barbato, F., & Zanuttigh, P. (2022) · Technologies · doi:10.3390/technologies10040090 · Cited by 58 · see citing works
UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation
Lee, T., Lee, B.-U., Shin, I., Choe, J., Shin, U., Kweon, I. S., & Yoon, K. (2022) · 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/cvpr52688.2022.01447 · Cited by 57 · see citing works
Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning
Liu, W., Luo, Z., Cai, Y., Yu, Y., Yang, K., Marcato, J., Gonçalves, W. N., & Li, J. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.04.012 · Cited by 55 · see citing works
Uncertainty-Aware Multi-Dimensional Mutual Learning for Brain and Brain Tumor Segmentation
Zhao, J., Xing, Z., Chen, Z., Wan, L., Han, T., Fu, H., & Zhu, L. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3274255 · Cited by 52 · see citing works
SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection
Zhang, W., Li, W., & Xu, D. (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/cvpr46437.2021.00670 · Cited by 50 · see citing works
EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation
Tan, M., Zhuang, Z., Chen, S., Li, R., Jia, K., Wang, Q., & Li, Y. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3402232 · Cited by 50 · see citing works
Cross-modal Orthogonal High-rank Augmentation for RGB-Event Transformer-trackers
Zhu, Z., Hou, J., & Wu, D. O. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02015 · Cited by 48 · see citing works
Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation
Li, L., Shum, H. P. H., & Breckon, T. P. (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/cvpr52729.2023.00903 · Cited by 47 · see citing works
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Lee, T., Tremblay, J., Blukis, V., Wen, B., Lee, B.-U., Shin, I., Birchfield, S. T., Kweon, I. S., & Yoon, K. (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/cvpr52729.2023.02039 · Cited by 41 · see citing works
ACSC: Automatic Calibration for Non-repetitive Scanning Solid-State LiDAR and Camera SystemsPreprint
Cui, J., Niu, J., Ouyang, Z., He, Y., & Liu, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08516 · Cited by 40 · see citing works
Multi-Modal Data-Efficient 3D Scene Understanding for Autonomous Driving
Kong, L., Xu, X., Ren, J., Zhang, W., Pan, L., Chen, K., Ooi, W. T., & Liu, Z. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3535625 · Cited by 43 · see citing works
OpenMPD: An Open Multimodal Perception Dataset for Autonomous Driving
Zhang, X., Li, Z., Gong, Y., Jin, D., Li, J., Wang, L., Zhu, Y., & Liu, H. (2022) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2022.3143173 · Cited by 39 · see citing works
Multipath Fusion Mask R-CNN With Double Attention and Its Application Into Gear Pitting Detection
Xi, D., Qin, Y., Luo, J., Pu, H., & Wang, Z. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3049276 · Cited by 36 · see citing works
Swinv2-Imagen: hierarchical vision transformer diffusion models for text-to-image generation
Li, R., Li, W., Yang, Y., Wei, H., Jiang, J., & Bai, Q. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09021-x · Cited by 33 · see citing works
3D point cloud descriptors: state-of-the-art
Han, X.-F., Feng, Z.-A., Sun, S., & Xiao, G. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10486-4 · Cited by 33 · see citing works
Bultmann, S., Quenzel, J., & Behnke, S. (2022) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2022.104286 · Cited by 32 · see citing works
Li, M., Zhang, Y., Xie, Y., Gao, Z., Li, C., Zhang, Z., & Qu, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547990 · Cited by 31 · see citing works
CL3D: Unsupervised Domain Adaptation for Cross-LiDAR 3D Detection
Peng, X., Zhu, X., & Ma, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i2.25297 · Cited by 30 · see citing works
Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning
Sun, T., Zhang, Z., Tan, X., Qu, Y., & Xie, Y. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3372449 · Cited by 29 · see citing works
Domain Adaptive LiDAR Point Cloud Segmentation With 3D Spatial Consistency
Xiao, A., Guan, D., Zhang, X., & Lu, S. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3335879 · Cited by 28 · see citing works
LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR
Bartoccioni, F., Zablocki, É., Pérez, P., Cord, M., & Alahari, K. (2022) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2022.103601 · Cited by 28 · see citing works
Jin, Z., Lei, Y., Akhtar, N., Li, H., & Hayat, M. (2022) · 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/cvpr52688.2022.00709 · Cited by 28 · see citing works
GIPSO: Geometrically Informed Propagation for Online Adaptation in 3D LiDAR Segmentation
Saltori, C., Krivosheev, E., Lathuilière, S., Sebe, N., Galasso, F., Fiameni, G., Ricci, E., & Poiesi, F. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19827-4_33 · Cited by 30 · see citing works
DetMatch: Two Teachers are Better than One for Joint 2D and 3D Semi-Supervised Object Detection
Park, J. S., Xu, C., Zhou, Y., Tomizuka, M., & Zhan, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20080-9_22 · Cited by 28 · see citing works
Xie, Y., Tian, J., & Zhu, X. X. (2022) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2022.103165 · Cited by 27 · see citing works
Zhang, Q., Hou, J., & Qian, Y. (2023) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3286981 · Cited by 27 · see citing works
Multi-Modal Continual Test-Time Adaptation for 3D Semantic Segmentation
Cao, H., Xu, Y., Yang, J., Yin, P., Yuan, S., & Xie, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01724 · Cited by 27 · see citing works
Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation
Saltori, C., Galasso, F., Fiameni, G., Sebe, N., Poiesi, F., & Ricci, E. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3310261 · Cited by 26 · see citing works
Multi-Space Alignments Towards Universal LiDAR Segmentation
Liu, Y., Kong, L., Wu, X., Chen, R., Li, X. Y., Pan, L., Liu, Z., & Ma, Y. (2024) · 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/cvpr52733.2024.01388 · Cited by 26 · see citing works
Multi-source multi-modal domain adaptation
Zhao, S., Jiang, J., Tang, W., Zhu, J., Chen, H., Xu, P., Schuller, B. W., Tao, J., Yao, H., & Ding, G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102862 · Cited by 26 · see citing works
Rochan, M., Aich, S., Corral-Soto, E. R., Nabatchian, A., & Liu, B. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9811654 · Cited by 28 · see citing works
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud SegmentationPreprint
Xu, C., Wu, B., Wang, Z., Zhan, W., Vajda, P., Keutzer, K., & Tomizuka, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.01803 · Cited by 26 · see citing works
LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation
Wang, S., Li, W., Liu, W., Liu, X., & Zhu, J. (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/cvpr52729.2023.00502 · Cited by 25 · see citing works
3D Point Cloud Descriptors in Hand-crafted and Deep Learning Age: State-of-the-ArtPreprint
Han, X.-F., Sun, S., Song, X., & Xiao, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.02297 · Cited by 43 · see citing works
Li, G., Kang, G., Wang, X., Wei, Y., & Yang, Y. (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/cvpr52729.2023.01960 · Cited by 24 · see citing works
Three Pillars Improving Vision Foundation Model Distillation for Lidar
Puy, G., Gidaris, S., Boulch, A., Siméoni, O., Sautier, C., Pérez, P., Bursuc, A., & Marlet, R. (2024) · 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/cvpr52733.2024.02033 · Cited by 25 · see citing works
Category-Level Adversaries for Outdoor LiDAR Point Clouds Cross-Domain Semantic Segmentation
Yuan, Z., Wen, C., Cheng, M., Su, Y., Liu, W., Yu, S., & Wang, C. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3219853 · Cited by 24 · see citing works
3D Semantic Segmentation of Aerial Photogrammetry Models Based on Orthographic Projection
Rong, M., & Shen, S. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3273224 · Cited by 24 · see citing works
Multi-Modal Contrastive Learning for LiDAR Point Cloud Rail-Obstacle Detection in Complex Weather
Wen, L., Peng, Y., Lin, M., Gan, N., & Tan, R. (2024) · Electronics · doi:10.3390/electronics13010220 · Cited by 25 · see citing works
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Michele, B., Boulch, A., Puy, G., Vu, T.-H., Marlet, R., & Courty, N. (2024) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00134 · Cited by 24 · see citing works
Self-citationDaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection
Althoupety, A. S., Wang, L., Feng, W., & Rekabdar, B. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00305 · Cited by 24 · see citing works
Modality adaptation in multimodal data
Razzaghi, P., Abbasi, K., Shirazi, M., & Shabani, N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115126 · Cited by 23 · see citing works
Zhang, Y., Li, M., Xie, Y., Li, C., Wang, C., Zhang, Z., & Qu, Y. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3547987 · Cited by 23 · see citing works
Instant Domain Augmentation for LiDAR Semantic Segmentation
Ryu, K., Hwang, S., & Park, J. (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/cvpr52729.2023.00902 · Cited by 23 · see citing works
Wu, Y., Xing, M., Zhang, Y., Xie, Y., Jianping Fan, B., Shi, Z., & Qu, Y. (2023) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3581783.3612013 · Cited by 22 · see citing works
DODA: Data-Oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation
Ding, R., Yang, J., Jiang, L., & Qi, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19812-0_17 · Cited by 21 · see citing works
Multi-Stage Fusion for Multi-Class 3D Lidar Detection
Wang, Z.-J., Zhao, Z. W., Jin, Z., Che, Z., Tang, J., Shen, C., & Peng, Y. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00347 · Cited by 20 · see citing works
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object DetectionPreprint
Hegde, D., Sindagi, V. A., Kilic, V., Cooper, A. B., Foster, M. A., & Patel, V. M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.14651 · Cited by 20 · see citing works
Robust 3D Semantic Segmentation Based on Multi-Phase Multi-Modal Fusion for Intelligent Vehicles
Ni, P., Li, X., Xu, W., Kong, D., Hu, Y., & Wei, K. (2023) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2023.3317784 · Cited by 21 · see citing works
U3DS 3 : Unsupervised 3D Semantic Scene Segmentation
Liu, J., Yu, Z., Breckon, T. P., & Shum, H. P. H. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00372 · Cited by 19 · see citing works
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 · see citing works
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 · see citing works
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 20 · see citing works
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 · see citing works
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 18 · see citing works
Wang, Q., Zhang, Y., & He, B. (2023) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2023.3247737 · Cited by 17 · see citing works
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 17 · see citing works
Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning
Hatem, A. S., Qian, Y., & Wang, Y. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01512 · Cited by 17 · see citing works
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 19 · see citing works
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 · see citing works
Cardace, A., Ramirez, P. Z., Salti, S., & Stefano, L. D. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00015 · Cited by 16 · see citing works
Walking Your LiDOG: A Journey Through Multiple Domains for LiDAR Semantic Segmentation
Saltori, C., Ošep, A., Ricci, E., & Leal-Taixé, L. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00025 · Cited by 17 · see citing works
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation
Pin, T., Xu, H., & Ma, C. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00309 · Cited by 16 · see citing works
MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation
Cao, H., Xu, Y., Yang, J., Yin, P., Yuan, S., & Xie, L. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610316 · Cited by 17 · see citing works
Traffic thermal infrared texture generation based on siamese semantic CycleGAN
Wang, P., Sun, H., Bai, X., Guo, S., & Jin, D. (2021) · Infrared Physics & Technology · doi:10.1016/j.infrared.2021.103748 · Cited by 16 · see citing works
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-Modal Distillation
Vobecký, A., Hurych, D., Siméoni, O., Gidaris, S., Bursuc, A., Pérez, P., & Šivic, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19839-7_28 · Cited by 15 · see citing works
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 · see citing works
OpenForest: a data catalog for machine learning in forest monitoring
Ouaknine, A., Kattenborn, T., Lalibert'e, E., & Rolnick, D. (2023) · Environmental Data Science · doi:10.1017/eds.2024.53 · Cited by 33 · see citing works
DFAN: Dual-Branch Feature Alignment Network for Domain Adaptation on Point Clouds
Shi, L., Yuan, Z., Cheng, M., Chen, Y., & Wang, C. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3171038 · Cited by 14 · see citing works
Efficient 3D Scene Semantic Segmentation via Active Learning on Rendered 2D Images
Rong, M., Cui, H., & Shen, S. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3286708 · Cited by 15 · see citing works
TPV-IGKD: Image-Guided Knowledge Distillation for 3D Semantic Segmentation With Tri-Plane-View
Li, J.-C., Lu, J., Wei, M., Kang, H.-Y., & Zhang, Q. (2024) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2024.3361163 · Cited by 14 · see citing works
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 · see citing works
- 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 · 2018: 1
- 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 › Engineering › Computational Mechanics › 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
