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arXiv · 2403.08919

CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow

Chenbin Pan·Burhaneddin Yaman·Senem Velipasalar·Liu Ren
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Abstract

Autonomous driving stands as a pivotal domain in computer vision, shaping the future of transportation. Within this paradigm, the backbone of the system plays a crucial role in interpreting the complex environment. However, a notable challenge has been the loss of clear supervision when it comes to Bird's Eye View elements. To address this limitation, we introduce CLIP-BEVFormer, a novel approach that leverages the power of contrastive learning techniques to enhance the multi-view image-derived BEV backbones with ground truth information flow. We conduct extensive experiments on the challenging nuScenes dataset and showcase significant and consistent improvements over the SOTA. Specifically, CLIP-BEVFormer achieves an impressive 8.5\% and 9.2\% enhancement in terms of NDS and mAP, respectively, over the previous best BEV model on the 3D object detection task.

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Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar, Liu Ren. 2024-03-13. CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow. https://arxiv.org/abs/2403.08919

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