Task: Scene Flow Estimation in Autonomous Driving.

📜 2024/07/24: Merging SeFlow & DeFlow code together, lighter setup and easier running.

🔥 2024/07/02: Check the self-supervised version in our new ECCV'24 SeFlow. The 1st ranking in new leaderboard among self-supervise methods.

Pre-trained weights for models are available in Zenodo. Check usage in 2. Evaluation or 3. Visualization.

Scripts quick view in our scripts:

• dataprocess/extract_*.py : pre-process data before training to speed up the whole training time. [Dataset we included now: Argoverse 2 and Waymo, more on the way: Nuscenes, custom data.]

• 1_train.py: Train the model and get model checkpoints. Pls remember to check the config.

• 2_eval.py : Evaluate the model on the validation/test set. And also upload to online leaderboard.

• 3_vis.py : For visualization of the results with a video.

💡: Want to learn how to add your own network in this structure? Check Contribute section and know more about the code. Fee free to pull request!

Environment: Clone the repo and build the environment, check detail installation for more information. Conda/Mamba is recommended.

git clone --recursive https://github.com/KTH-RPL/DeFlow.git
cd DeFlow
mamba env create -f environment.yaml

CUDA package (need install nvcc compiler), the compile time is around 1-5 minutes:

mamba activate deflow

# change it if you use different cuda version (I tested 11.3, 11.4, 11.7, 11.8 all works)
export PATH=/usr/local/cuda-11.7/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH

cd assets/cuda/mmcv && python ./setup.py install && cd ../../..

Or another environment setup choice is Docker which isolated environment, you can pull it by. If you have different arch, please build it by yourself cd DeFlow && docker build -t zhangkin/seflow by going through build-docker-image section.

# option 1: pull from docker hub
docker pull zhangkin/seflow

# run container
docker run -it --gpus all -v /dev/shm:/dev/shm -v /home/kin/data:/home/kin/data --name deflow zhangkin/seflow /bin/zsh
# then `mamba activate seflow` python environment is ready to use

Note: Prepare raw data and process train data only needed run once for the task. No need repeat the data process steps till you delete all data. We use wandb to log the training process, and you may want to change all entity="kth-rpl" to your own entity.

Check dataprocess/README.md for downloading tips for the raw Argoverse 2 dataset. Or maybe you want to have the mini processed dataset to try the code quickly, We directly provide one scene inside train and val. It already converted to .h5 format and processed with the label data. You can download it from Zenodo and extract it to the data folder. And then you can skip following steps and directly run the training script.

wget https://zenodo.org/record/12751363/files/demo_data.zip
unzip demo_data.zip -p /home/kin/data/av2

Checking more information (download raw data etc) in dataprocess/README.md. Extract all data to unified h5 format. [Runtime: Normally need 10 mins finished run following commands totally in my desktop, 45 mins for the cluster I used]

python dataprocess/extract_av2.py --av2_type sensor --data_mode train --argo_dir /home/kin/data/av2 --output_dir /home/kin/data/av2/preprocess_v2
python dataprocess/extract_av2.py --av2_type sensor --data_mode val --mask_dir /home/kin/data/av2/3d_scene_flow
python dataprocess/extract_av2.py --av2_type sensor --data_mode test --mask_dir /home/kin/data/av2/3d_scene_flow

All local benchmarking methods and ablation studies can be done through command with different config, check assets/slurm for all the commands we used in our experiments.

Best fine-tuned model train with following command by other default config in conf/config.yaml and conf/model/deflow.yaml, if you will set wandb_mode=online, maybe change all entity="kth-rpl" to your own account name.

python 1_train.py model=deflow lr=2e-4 epochs=20 batch_size=16 loss_fn=deflowLoss
python 1_train.py model=fastflow3d lr=2e-4 epochs=20 batch_size=16 loss_fn=ff3dLoss

To help community benchmarking, we provide our weights including fastflow3d, deflow in Zendo. These checkpoints also include parameters and status of that epoch inside it. If you are interested in weights of ablation studies, please contact us.

You can view Wandb dashboard for the training and evaluation results or run/submit to av2 leaderboard to get official results follow below steps.

Since in training, we save all hyper-parameters and model checkpoints, the only thing you need to do is to specify the checkpoint path. Remember to set the data path correctly also.

# downloaded pre-trained weight, or train by yourself
wget https://zenodo.org/records/12632962/files/deflow_best.ckpt

python 2_eval.py checkpoint=/home/kin/deflow_best.ckpt av2_mode=val # it will directly prints all metric
# it will output a command with absolute path of a zip file for you to submit to leaderboard
python 2_eval.py checkpoint=/home/kin/deflow_best.ckpt av2_mode=test leaderboard_version=1
python 2_eval.py checkpoint=/home/kin/deflow_best.ckpt av2_mode=test leaderboard_version=2

Check all detailed result files (presented in our paper Table 1) in this discussion.

To submit to the Online Leaderboard, if you select av2_mode=test, it should be a zip file for you to submit to the leaderboard. Note: The leaderboard result in DeFlow main paper is version 1, as version 2 is updated after DeFlow paper.

# since the env may conflict we set new on deflow, we directly create new one:
mamba create -n py37 python=3.7
mamba activate py37
pip install "evalai"

# Step 2: login in eval and register your team
evalai set-token <your token>

# Step 3: Copy the command pop above and submit to leaderboard
evalai challenge 2010 phase 4018 submit --file av2_submit.zip --large --private
evalai challenge 2210 phase 4396 submit --file av2_submit_v2.zip --large --private

We provide a script to visualize the results of the model. You can specify the checkpoint path and the data path to visualize the results. The step is quickly similar to evaluation.

# downloaded pre-trained weight, or train by yourself
wget https://zenodo.org/records/12632962/files/deflow_best.ckpt

python 3_vis.py checkpoint=/home/kin/deflow_best.ckpt dataset_path=/home/kin/data/av2/preprocess/sensor/vis

# Then terminal will tell you the command you need run. For example here is the output of the above:
Model: DeFlow, Checkpoint from: /home/kin/deflow_best.ckpt
We already write the estimate flow: deflow_best into the dataset, please run following commend to visualize the flow. Copy and paste it to your terminal:
python tests/scene_flow.py --flow_mode 'deflow_best' --data_dir /home/kin/data/av2/preprocess/sensor/mini
Enjoy! ^v^ ------ 

# Then run the command in the terminal:
python tests/scene_flow.py --flow_mode 'deflow_best' --data_dir /home/kin/data/av2/preprocess/sensor/mini

@article{zhang2024deflow,
  author={Zhang, Qingwen and Yang, Yi and Fang, Heng and Geng, Ruoyu and Jensfelt, Patric},
  title={DeFlow: Decoder of Scene Flow Network in Autonomous Driving},
  journal={arXiv preprint arXiv:2401.16122},
  year={2024}
}
@article{zhang2024seflow,
  author={Zhang, Qingwen and Yang, Yi and Li, Peizheng and Andersson, Olov and Jensfelt, Patric},
  title={SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving},
  journal={arXiv preprint arXiv:2407.01702},
  year={2024}
}

This implementation is based on codes from several repositories. Thanks to these authors who kindly open-sourcing their work to the community. Please see our paper reference part to get more information. Thanks to Kyle Vedder (ZeroFlow) who kindly discussed their results with us and HKUST Ramlab's member: Jin Wu who gave constructive comments on this work. The computations were enabled by the supercomputing resource Berzelius provided by National Supercomputer Centre at Linköping University and the Knut and Alice Wallenberg Foundation, Sweden.

❤️: ZeroFlow, NSFP, FastNSF. Others good code style and tools: forecast-mae, kiss-icp