(AAAI2024) Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving

Introduction

This is the official pytorch implementation of Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving, In AAAI'24, Junkai Xu, Liang Peng, Haoran Cheng, Linxuan Xia, Qi Zhou, Dan Deng, Wei Qian, Wenxiao Wang and Deng Cai.

[Paper]

News

[2023-12-20] Paper is released on arxiv!
[2023-12-19] Code is released.
[2023-12-14] Demo release.
[2023-12-9] Vampire is accepted at AAAI 2024!! Code is comming soon.

Demo

Quick Start

Installation

Step 0. Install pytorch (v1.9.0).

conda create --name vampire python=3.7 -y
conda activate vampire
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Step 1. Install mmdet (2.26.0), mmsegmentation (0.29.1)MMDetection3D (v1.0.0rc6).

pip install openmim==0.3.3
mim install mmcv-full==1.6.2
mim install mmdet==2.26.0
mim install mmsegmentation==0.29.1
git clone https://github.com/open-mmlab/mmdetection3d.git --branch v1.0.0rc6 --single-branch
cd mmdetection3d
pip install -e .
cd ..

Step 2. Install requirements.

pip install -r requirements.txt
python setup.py develop

Data preparation

Step 0. Download nuScenes official dataset and occupancy trainval subset (including gts.tar.gz and annotations.json)

Step 1. Unzip all data in your disk and Symlink the dataset root to ./data/.

ln -s [nuscenes root] ./data/

The directory will be as follows.

Vampire/
├── data/
│   ├── nuScenes/
│   │   ├── maps/
│   │   ├── samples/
│   │   ├── sweeps/
|   |   ├── lidarseg/
|   |   ├── panoptic/
│   │   ├── v1.0-test/
|   |   ├── v1.0-trainval/
|   |   ├── gts/
|   |   ├── annotations.json

Step 2. Prepare infos.

python scripts/gen_info.py

Tutorials

Train on 8 NVIDIA GPUs with a total batch size of 8.

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

Validation & Test (output submit file for nuscenes toolkit evaluation)

python [EXP_PATH] --ckpt_path [CKPT_PATH] -v -b 8 --gpus 8
python [EXP_PATH] --ckpt_path [CKPT_PATH] -t -b 8 --gpus 8

Pretrained Models

Exp	Occ.	Seg.	Det.	weights
Vampire	25.8	62.6	0.318	Google-drive

Citation

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

@article{xu2023regulating,
      title={Regulating Intermediate 3D Features for Vision-Centric Autonomous Driving}, 
      author={Xu, Junkai and Peng, Liang and Cheng, Haoran and Xia, Linxuan and Zhou, Qi and Deng, Dan and Qian, Wei and Wang, Wenxiao and Cai, Deng},
      journal={arXiv preprint arXiv:2312.11837},
      year={2023},
}

Acknowledgements

This project benefits from the following codebases. Thanks for their great works!

cskkxjk / Vampire