BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning
We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-down-view representation of the surrounding environment, making it crucial for 3D object detection, segmentation, and...
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| Published in: | IEEE robotics and automation letters Vol. 10; no. 4; pp. 1 - 7 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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IEEE
01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 2377-3766, 2377-3766 |
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| Abstract | We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-down-view representation of the surrounding environment, making it crucial for 3D object detection, segmentation, and trajectory prediction tasks. While prior work has primarily focused on enhancing BEV encoders and task-specific heads, we address the underexplored potential of representation learning in BEV models. BEVCon introduces two contrastive learning modules: an instance feature contrast module for refining BEV features and a perspective view contrast module that enhances the image backbone. The dense contrastive learning designed on top of detection losses leads to improved feature representations across both the BEV encoder and the backbone. Extensive experiments on the nuScenes dataset demonstrate that BEVCon achieves consistent performance gains, achieving up to +2.4% mAP improvement over state-of-the-art baselines. Our results highlight the critical role of representation learning in BEV perception and offer a complementary avenue to conventional task-specific optimizations. Code and models are available at https://github.com/matthew-leng/BEVCon . |
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| AbstractList | We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-down-view representation of the surrounding environment, making it crucial for 3D object detection, segmentation, and trajectory prediction tasks. While prior work has primarily focused on enhancing BEV encoders and task-specific heads, we address the underexplored potential of representation learning in BEV models. BEVCon introduces two contrastive learning modules: an instance feature contrast module for refining BEV features and a perspective view contrast module that enhances the image backbone. The dense contrastive learning designed on top of detection losses leads to improved feature representations across both the BEV encoder and the backbone. Extensive experiments on the nuScenes dataset demonstrate that BEVCon achieves consistent performance gains, achieving up to +2.4% mAP improvement over state-of-the-art baselines. Our results highlight the critical role of representation learning in BEV perception and offer a complementary avenue to conventional task-specific optimizations. We present BEVCon, a simple yet effective contrastive learning framework designed to improve Bird's Eye View (BEV) perception in autonomous driving. BEV perception offers a top-down-view representation of the surrounding environment, making it crucial for 3D object detection, segmentation, and trajectory prediction tasks. While prior work has primarily focused on enhancing BEV encoders and task-specific heads, we address the underexplored potential of representation learning in BEV models. BEVCon introduces two contrastive learning modules: an instance feature contrast module for refining BEV features and a perspective view contrast module that enhances the image backbone. The dense contrastive learning designed on top of detection losses leads to improved feature representations across both the BEV encoder and the backbone. Extensive experiments on the nuScenes dataset demonstrate that BEVCon achieves consistent performance gains, achieving up to +2.4% mAP improvement over state-of-the-art baselines. Our results highlight the critical role of representation learning in BEV perception and offer a complementary avenue to conventional task-specific optimizations. Code and models are available at https://github.com/matthew-leng/BEVCon . |
| Author | Leng, Ziyang Yang, Jiawei Ren, Zhicheng Zhou, Bolei |
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| Cites_doi | 10.1109/CVPR42600.2020.01164 10.5555/3495724.3497510 10.1109/ICCV51070.2023.00310 10.1109/CVPR52729.2023.01385 10.5555/3524938.3525087 10.1109/ICCV51070.2023.00637 10.1109/ICCV51070.2023.00302 10.1007/978-3-031-19812-0_31 10.1109/ICCV48922.2021.00986 10.1007/978-3-030-58568-6_12 10.1109/CVPR.2009.5206848 10.1109/ICCV51070.2023.00335 10.1109/CVPR42600.2020.00975 10.1109/CVPR42600.2020.00252 10.1109/CVPR52729.2023.01712 10.1109/ICCV51070.2023.00575 10.1109/LRA.2020.3004325 10.1109/LRA.2022.3146898 10.1109/ICCV48922.2021.00718 10.1007/978-3-030-58452-8_13 10.1109/ICRA48891.2023.10160968 10.1609/aaai.v37i2.25233 10.1109/ICCV.2017.322 10.1007/978-3-031-20077-9_1 10.1609/aaai.v37i1.25185 10.1007/978-3-031-19809-0_7 10.1109/ICRA48891.2023.10160831 10.1109/CVPR.2016.90 10.1109/ICCV48922.2021.00811 10.1109/CVPR52729.2023.01710 10.1109/ICRA57147.2024.10611615 |
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| SubjectTerms | and Categorization Annotations Autonomous vehicles Coders Contrastive learning Deep Learning for Visual Perception Feature extraction Head Learning Modules Object detection Object recognition Perception Representation learning Representations Segmentation Three-dimensional displays Training Transforms |
| Title | BEVCon: Advancing Bird's Eye View Perception with Contrastive Learning |
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