Cross View Transformers

This repository contains the source code and data for our paper:

Cross-view Transformers for real-time Map-view Semantic Segmentation
Brady Zhou, Philipp Krähenbühl
CVPR 2022

Demos

Installation

# Clone repo
git clone https://github.com/bradyz/cross_view_transformers.git

cd cross_view_transformers

# Setup conda environment
conda create -y --name cvt python=3.8

conda activate cvt
conda install -y pytorch torchvision cudatoolkit=11.3 -c pytorch

# Install dependencies
pip install -r requirements.txt
pip install -e .

Data

Documentation:

• Dataset setup
• Label generation (optional)

Download the original datasets and our generated map-view labels

The structure of the extracted data should look like the following

/datasets/
├─ nuscenes/
│  ├─ v1.0-trainval/
│  ├─ v1.0-mini/
│  ├─ samples/
│  ├─ sweeps/
│  └─ maps/
│     ├─ basemap/
│     └─ expansion/
└─ cvt_labels_nuscenes/
   ├─ scene-0001/
   ├─ scene-0001.json
   ├─ ...
   ├─ scene-1000/
   └─ scene-1000.json

When everything is setup correctly, check out the dataset with

python3 scripts/view_data.py \
  data=nuscenes \
  data.dataset_dir=/media/datasets/nuscenes \
  data.labels_dir=/media/datasets/cvt_labels_nuscenes \
  data.version=v1.0-mini \
  visualization=nuscenes_viz \
  +split=val

Training

             

An average job of 50k training iterations takes ~8 hours.
Our models were trained using 4 GPU jobs, but also can be trained on single GPU.

To train a model,

python3 scripts/train.py \
  +experiment=cvt_nuscenes_vehicle
  data.dataset_dir=/media/datasets/nuscenes \
  data.labels_dir=/media/datasets/cvt_labels_nuscenes

For more information, see

• config/config.yaml - base config
• config/model/cvt.yaml - model architecture
• config/experiment/cvt_nuscenes_vehicle.yaml - additional overrides

Additional Information

Awesome Related Repos

• https://github.com/wayveai/fiery
• https://github.com/nv-tlabs/lift-splat-shoot
• https://github.com/tom-roddick/mono-semantic-maps

License

This project is released under the MIT license

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{zhou2022cross,
    title={Cross-view Transformers for real-time Map-view Semantic Segmentation},
    author={Zhou, Brady and Kr{\"a}henb{\"u}hl, Philipp},
    booktitle={CVPR},
    year={2022}
}