This repository is an official implementation of StreamMapNet.
Step 1. Create conda environment and activate it.
conda create --name streammapnet python=3.8 -y
conda activate streammapnet
Step 2. Install PyTorch.
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
Step 3. Install MMCV series.
# Install mmcv-series
pip install mmcv-full==1.6.0
pip install mmdet==2.28.2
pip install mmsegmentation==0.30.0
git clone https://github.com/open-mmlab/mmdetection3d.git
cd mmdetection3d
git checkout v1.0.0rc6
pip install -e .
Step 4. Install other requirements.
pip install -r requirements.txt
Step 1. Download NuScenes dataset to ./datasets/nuScenes
.
Step 2. Download Argoverse2 (sensor) dataset to ./datasets/av2
.
Step 3. Generate annotation files for NuScenes dataset.
python tools/nuscenes_converter.py --data-root ./datasets/nuScenes --newsplit
Step 4. Generate annotation files for Argoverse2 dataset.
python tools/argoverse_converter.py --data-root ./datasets/av2 --newsplit
To train a model with 8 GPUs:
bash tools/dist_train.sh ${CONFIG} 8
To validate a model with 8 GPUs:
bash tools/dist_test.sh ${CONFIG} ${CEHCKPOINT} 8 --eval
To test a model's inference speed:
python tools/benchmark.py ${CONFIG} ${CEHCKPOINT}
Range | Config | Epoch | Checkpoint | ||||
---|---|---|---|---|---|---|---|
57.9 | 55.7 | 61.3 | 58.3 | Config | 30 | ckpt | |
60.0 | 45.9 | 48.9 | 51.6 | Config | 30 | ckpt |
Range | Config | Epoch | Checkpoint | ||||
---|---|---|---|---|---|---|---|
32.2 | 29.3 | 40.8 | 34.1 | Config | 24 | ckpt | |
25.6 | 17.4 | 24.3 | 22.4 | Config | 24 | ckpt |
Range | Config | Epoch | Checkpoint | ||||
---|---|---|---|---|---|---|---|
61.7 | 66.3 | 62.1 | 63.4 | Config | 30 | ckpt |
If you find our paper or codebase useful in your research, please give us a star and cite our paper.
@InProceedings{Yuan_2024_streammapnet,
author = {Yuan, Tianyuan and Liu, Yicheng and Wang, Yue and Wang, Yilun and Zhao, Hang},
title = {StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction},
booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
month = {January},
year = {2024},
pages = {7356-7365}
}