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

• URL: http://arxiv.org/abs/2403.08919v2
• Date: Sat, 23 Nov 2024 21:33:56 GMT
• Title: CLIP-BEVFormer: Enhancing Multi-View Image-Based BEV Detector with Ground Truth Flow
• Authors: Chenbin Pan, Burhaneddin Yaman, Senem Velipasalar, Liu Ren,
• Abstract summary: We introduce CLIP-BEVFormer, a novel approach 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.
• Score: 20.550935390111686
• License:
• 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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