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bevdepth's Introduction

BEVDepth

BEVDepth is a new 3D object detector with a trustworthy depth estimation. For more details, please refer to our paper on Arxiv.

Updates!!

  • 【2022/06/21】 We have released our paper on Arxiv.

Quick Start

Installation

Step 0. Install pytorch(v1.9.0).

Step 1. Install MMDetection3D(v0.18.0).

Step 2. Install requirements.

pip install -r requirements.txt

Step 3. Install BEVDepth(gpu required).

python setup.py develop

Data preparation

Step 0. Download nuScenes official dataset.

Step 1. Symlink the dataset root to ./data/.

ln -s [nuscenes root] ./data/

The directory will be as follows.

BEVDepth
├── data
│   ├── nuScenes
│   │   ├── maps
│   │   ├── samples
│   │   ├── sweeps
│   │   ├── v1.0-test
|   |   ├── v1.0-trainval

Step 2. Prepare infos.

python scripts/gen_info.py

Step 3. Prepare depth gt.

python scripts/gen_depth_gt.py

Tutorials

Train.

python [EXP_PATH] --amp_backend native -b 8 --gpus 8

Eval.

python [EXP_PATH] --ckpt_path [CKPT_PATH] -e -b 8 --gpus 8

Benchmark

Backbone mAP mATE mASE mAOE mAVE mAAE NDS weights
R50 0.3329 0.6832 0.2761 0.5446 0.5258 0.2259 0.4409 github

Cite BEVDepth

If you use BEVDepth in your research, please cite our work by using the following BibTeX entry:

 @article{li2022bevdepth,
  title={BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection},
  author={Li, Yinhao and Ge, Zheng and Yu, Guanyi and Yang, Jinrong and Wang, Zengran and Shi, Yukang and Sun, Jianjian and Li, Zeming},
  journal={arXiv preprint arXiv:2206.10092},
  year={2022}
}

bevdepth's People

Contributors

yinchimaoliang avatar

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