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BEVPoolv2: A Cutting-edge Implementation of BEVDet Toward Deployment

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arxiv 2211.17111 v1 pith:U5HKEI6F submitted 2022-11-30 cs.CV

Junjie Huang , Guan Huang This is my paper
classification cs.CV
keywords bevdetdev2backendbevpoolv2deploymentimplementationlargeprocessed
verification ladder T0 review T1 audit T2 compute T3 formal
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We release a new codebase version of the BEVDet, dubbed branch dev2.0. With dev2.0, we propose BEVPoolv2 upgrade the view transformation process from the perspective of engineering optimization, making it free from a huge burden in both calculation and storage aspects. It achieves this by omitting the calculation and preprocessing of the large frustum feature. As a result, it can be processed within 0.82 ms even with a large input resolution of 640x1600, which is 15.1 times the previous fastest implementation. Besides, it is also less cache consumptive when compared with the previous implementation, naturally as it no longer needs to store the large frustum feature. Last but not least, this also makes the deployment to the other backend handy. We offer an example of deployment to the TensorRT backend in branch dev2.0 and show how fast the BEVDet paradigm can be processed on it. Other than BEVPoolv2, we also select and integrate some substantial progress that was proposed in the past year. As an example configuration, BEVDet4D-R50-Depth-CBGS scores 52.3 NDS on the NuScenes validation set and can be processed at a speed of 16.4 FPS with the PyTorch backend. The code has been released to facilitate the study on https://github.com/HuangJunJie2017/BEVDet/tree/dev2.0.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Mapping like a Skeptic: Probabilistic BEV Projection for Online HD Mapping

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A probabilistic BEV projection with learned offsets and confidence-based temporal fusion improves online HD map accuracy and generalization.

  2. PriorFusion: Unified Integration of Priors for Robust Road Perception in Autonomous Driving

    cs.CV 2025-07 conditional novelty 5.0 of 10

    PriorFusion integrates semantic segmentation, SVD-based shape templates, and a truncated diffusion decoder to improve vectorized road element perception, reporting state-of-the-art mAP on nuScenes.

  3. GTAD: Global Temporal Aggregation Denoising Learning for 3D Semantic Occupancy Prediction

    cs.CV 2025-07 conditional novelty 5.0 of 10

    GTAD combines an in-model latent denoising network with global temporal interaction to improve camera-based 3D semantic occupancy prediction, reporting 40.76 mIoU on Occ3D-nuScenes at 12 epochs.

  4. DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos

    cs.CV 2025-06 conditional novelty 5.0 of 10

    DySS combines state-space feature learning with dynamic query merging and pruning to improve both accuracy and speed for camera-based 3D detection on nuScenes.

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