DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866917690954743808 |
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| author | Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong |
| author_facet | Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong |
| contents | Closing the domain gap between training and deployment and incorporating multiple sensor modalities are two challenging yet critical topics for self-driving. Existing work only focuses on single one of the above topics, overlooking the simultaneous domain and modality shift which pervasively exists in real-world scenarios. A model trained with multi-sensor data collected in Europe may need to run in Asia with a subset of input sensors available. In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird's-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain. This work results in the first open analysis of cross-domain cross-sensor perception and adaptation for monocular 3D tasks in the wild. We benchmark our approach on large-scale datasets under a wide range of domain shifts and show state-of-the-art results against various baselines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_03724 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception Man, Yunze Gui, Liang-Yan Wang, Yu-Xiong Computer Vision and Pattern Recognition Artificial Intelligence Robotics Closing the domain gap between training and deployment and incorporating multiple sensor modalities are two challenging yet critical topics for self-driving. Existing work only focuses on single one of the above topics, overlooking the simultaneous domain and modality shift which pervasively exists in real-world scenarios. A model trained with multi-sensor data collected in Europe may need to run in Asia with a subset of input sensors available. In this work, we propose DualCross, a cross-modality cross-domain adaptation framework to facilitate the learning of a more robust monocular bird's-eye-view (BEV) perception model, which transfers the point cloud knowledge from a LiDAR sensor in one domain during the training phase to the camera-only testing scenario in a different domain. This work results in the first open analysis of cross-domain cross-sensor perception and adaptation for monocular 3D tasks in the wild. We benchmark our approach on large-scale datasets under a wide range of domain shifts and show state-of-the-art results against various baselines. |
| title | DualCross: Cross-Modality Cross-Domain Adaptation for Monocular BEV Perception |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2305.03724 |