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RecurrentBEV

[ECCV 2024] RecurrentBEV: A Long-term Temporal Fusion Framework for Multi-view 3D Detection


Introduction

This repository is an official implementation of RecurrentBEV. It is built based on MMDetection3D.

Main Results

NuScenes Val Set

Backbone Img Size Pretrain NDS mAP Config Download
Res50 256x704 ImageNet 54.9 44.5 config model
Res101 512x1408 ImageNet 59.9 50.9 config -
Res101 512x1408 NuImages 61.2 52.8 config model

NuScenes Test Set

Backbone Img Size Pretrain NDS mAP Config Download
V2-99 640x1600 DD3D 65.1 57.3 config model
ConvNeXt-B 640x1600 COCO 65.1 57.4 config -

Inference Speed

The below table shows end-to-end FPS (Frames Per Second) of RecurrentBEV measured with a single RTX-3090.

Method Pytorch-FP32 TensorRT-FP32 TensorRT-FP16 TensorRT-INT8
RecurrentBEV 25.6 46.3 129.3 234.8
StreamPETR 26.7 53.9 134.6 167.4

Getting Started

Please follow our documentation step by step. If you like our work, please recommend it to your colleagues and friends.

  1. Environment Setup.

  2. Data Preparation.

  3. Training and Inference.

  4. Visualization.

  5. Deployment.

Features List

  • RecurretBEV code
  • Visualization
  • Convert to TRT model
  • TensorRT inference

Acknowledgements

We thank these great works and open-source codebases:

Citation

If you find RecurrentBEV is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.