Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911770930577408 |
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| author | Huang, Yihao Yu, Kaiyuan Guo, Qing Juefei-Xu, Felix Jia, Xiaojun Li, Tianlin Pu, Geguang Liu, Yang |
| author_facet | Huang, Yihao Yu, Kaiyuan Guo, Qing Juefei-Xu, Felix Jia, Xiaojun Li, Tianlin Pu, Geguang Liu, Yang |
| contents | In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such as fog, rain, snow, and sunlight in the intricate physical world remains underexplored. In this paper, we evaluate the robustness of fusion models from the perspective of fusion strategies on the corrupted dataset. Based on the evaluation, we further propose a concise yet practical fusion strategy to enhance the robustness of the fusion models, namely flexibly weighted fusing features from LiDAR and camera sources to adapt to varying weather scenarios. Experiments conducted on four types of fusion models, each with two distinct lightweight implementations, confirm the broad applicability and effectiveness of the approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02738 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective Huang, Yihao Yu, Kaiyuan Guo, Qing Juefei-Xu, Felix Jia, Xiaojun Li, Tianlin Pu, Geguang Liu, Yang Computer Vision and Pattern Recognition Machine Learning In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such as fog, rain, snow, and sunlight in the intricate physical world remains underexplored. In this paper, we evaluate the robustness of fusion models from the perspective of fusion strategies on the corrupted dataset. Based on the evaluation, we further propose a concise yet practical fusion strategy to enhance the robustness of the fusion models, namely flexibly weighted fusing features from LiDAR and camera sources to adapt to varying weather scenarios. Experiments conducted on four types of fusion models, each with two distinct lightweight implementations, confirm the broad applicability and effectiveness of the approach. |
| title | Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2402.02738 |