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Preparing Argoverse2 Dataset

ArgoVerse2 is a large-scale dataset for autonomous driving research. Before using the dataset, please carefully read and comply with the ArgoVerse dataset Terms of Use.

For more information, visit the ArgoVerse2 webpage.

1. Install the Environment

To install the development toolkit, we follow the official setup instructions. Note that the installation process requires manually installing Rust via rustup before proceeding with the PyPI installation.

  1. Install Rust via rustup:

    curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
export PATH=$HOME/.cargo/bin:$PATH
rustup default nightly

  2. Install our modified av2 API:

    <!-- pip install git+https://github.com/ziyc/av2-api -->
pip install av2
pip install "numpy==1.23.5"

    Note: This is a modified API forked from the original av2-api. Our version allows loading tracking IDs of objects in the sensor dataset.

2. Download the raw data

  1. Install s5cmd following the instructions.

  2. Set Up the Data Directory

    # Create the data directory or create a symbolic link to the data directory
mkdir -p ./data/argoverse/raw  
mkdir -p ./data/argoverse/processed

  3. Download the dataset:

     # Set the name of the dataset subset you want to download
 export DATASET_NAME="sensor"

 # Set the target directory where you want to save the dataset
 export TARGET_DIR="data/argoverse/raw"

 # Create the target directory if it doesn't exist
 mkdir -p $TARGET_DIR

 # Download the dataset using s5cmd
 s5cmd --no-sign-request cp "s3://argoverse/datasets/av2/$DATASET_NAME/*" $TARGET_DIR

3. Process the Data

After downloading the raw dataset, you'll need to preprocess these data to our desired format. For the ArgoVerse2 Dataset, we first organize the scene names alphabetically and store them in data/argoverse_train_list.txt. The scene index is then determined by the line number minus one.

Running the preprocessing script

You can process a specific range of scenes:

# export PYTHONPATH=\path\to\project
python -m datasets.preprocess \
    --data_root data/argoverse/raw \
    --target_dir data/argoverse/processed/training \
    --dataset argoverse \
    --start_idx 0 \
    --num_scenes 100 \
    --workers 64 \
    --process_keys images lidar calib pose dynamic_masks objects

The extracted data will be stored in the data/argoverse/processed directory.

4. Extract Masks (the same as Waymo Open Dataset)

To generate:

  • sky masks (required)
  • fine dynamic masks (optional)

Follow these steps:

Install SegFormer (Skip if already installed)

⚠️ SegFormer relies on mmcv-full=1.2.7, which relies on pytorch=1.8 (pytorch<1.9). Hence, a separate conda env is required.

#-- Set conda env
conda create -n segformer python=3.8
conda activate segformer
pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

#-- Install mmcv-full
pip install timm==0.3.2 pylint debugpy opencv-python-headless attrs ipython tqdm imageio scikit-image omegaconf
pip install mmcv-full==1.2.7 --no-cache-dir

#-- Clone and install segformer
git clone https://github.com/NVlabs/SegFormer
cd SegFormer
pip install .

Download the pretrained model segformer.b5.1024x1024.city.160k.pth from the google_drive / one_drive links in https://github.com/NVlabs/SegFormer#evaluation .

Remember the location where you download into, and pass it to the script in the next step with --checkpoint.

Run Mask Extraction Script

 conda activate segformer
 segformer_path=/path/to/segformer

 python datasets/tools/extract_masks.py \
    --data_root data/argoverse/processed/training \
    --segformer_path=$segformer_path \
    --checkpoint=$segformer_path/pretrained/segformer.b5.1024x1024.city.160k.pth \
    --split_file data/argoverse_example_scenes.txt \
    --process_dynamic_mask

Replace /path/to/segformer with the actual path to your Segformer installation.

Note: The --process_dynamic_mask flag is included to process fine dynamic masks along with sky masks.

This process will extract the required masks from your processed data.

5 Data Structure

After completing all preprocessing steps, the project files should be organized according to the following structure:

ProjectPath/data/
  └── argoverse/
    ├── raw/
    │    └── train/
    │         ├── 00a6ffc1-6ce9-3bc3-a060-6006e9893a1a/
    │         └──....
    └── processed/
         └──training/
              ├── 000/
              │  ├──images/             # Images: {timestep:03d}_{cam_id}.jpg
              │  ├──lidar/              # LiDAR data: {timestep:03d}.bin
              │  ├──ego_pose/           # Ego vehicle poses: {timestep:03d}.txt
              │  ├──extrinsics/         # Camera extrinsics: {cam_id}.txt
              │  ├──intrinsics/         # Camera intrinsics: {cam_id}.txt
              │  ├──sky_masks/          # Sky masks: {timestep:03d}_{cam_id}.png
              │  ├──dynamic_masks/      # Dynamic masks: {timestep:03d}_{cam_id}.png
              │  ├──fine_dynamic_masks/ # (Optional) Fine dynamic masks: {timestep:03d}_{cam_id}.png
              │  ├──objects/            # Object information
              │  └──humanpose/          # Preprocessed human body pose: smpl.pkl
              ├── 001/
              └── ...