Skip to content

Latest commit

 

History

History
173 lines (123 loc) · 6.59 KB

Pandaset.md

File metadata and controls

173 lines (123 loc) · 6.59 KB
Raw

Preparing PandaSet

Before downloading or using the Pandaset dataset, carefully read and agree to Pandaset's terms of use and licensing requirements.

For more information, visit the PandaSet website

1. Download and Organize Dataset

Due to reported issues with the official PandaSet download link, we recommend an alternative download method via Kaggle. However, please note that the original data and all usage rights are provided by PandaSet.

  1. Install the Kaggle API:

    If you haven't already, install the Kaggle API. You can do this using pip:

    pip install kaggle

    NOTE: Ensure you've set up your Kaggle API credentials as per their instructions.

  2. Download the dataset:

    Run the following command in your terminal:

    kaggle datasets download pz19930809/pandaset

  3. Organize the dataset:

    After downloading, organize the files with these commands:

    # Create the data directory or create a symbolic link to the data directory
mkdir -p ./data/pandaset/raw
mv pandaset-dataset.zip ./data/pandaset/raw
cd ./data/pandaset/raw
unzip pandaset-dataset.zip
rm pandaset-dataset.zip

2. Install PandaSet Development Toolkit

Install the modified PandaSet development toolkit:

git clone https://github.com/ziyc/pandaset-devkit.git
cd pandaset-devkit/python
pip install -e .

NOTE: This fork of the original devkit addresses file name mismatches for compatibility with our scripts.

3. Process Raw Data

You can provide a split file (e.g. data/pandaset_example_scenes.txt) to process a batch of scenes at once:

# export PYTHONPATH=\path\to\project
python datasets/preprocess.py \
    --data_root data/pandaset/raw \
    --target_dir data/pandaset/processed \
    --dataset pandaset \
    --split_file data/pandaset_example_scenes.txt \
    --workers 32 \
    --process_keys images lidar calib pose dynamic_masks objects

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

4. Extract Masks

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/pandaset/processed \
    --segformer_path=$segformer_path \
    --checkpoint=$segformer_path/pretrained/segformer.b5.1024x1024.city.160k.pth \
    --split_file data/pandaset_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. Human Body Pose Processing

SMPL-Nodes (SMPL Gaussian Representation) requires Human Body Pose Sequences of #### Prerequisites To utilize the SMPL-Gaussian to model pedestrians, please first download the SMPL models.

  1. Download SMPL v1.1 (SMPL_python_v.1.1.0.zip) from the SMPL official website
  2. Move SMPL_python_v.1.1.0/smpl/models/basicmodel_neutral_lbs_10_207_0_v1.1.0.pkl to PROJECT_ROOT/smpl_models/SMPL_NEUTRAL.pkl

SMPL-Nodes (SMPL-Gaussian Representation) requires Human Body Pose Sequences of pedestrians. We've developed a human body pose processing pipeline for in-the-wild driving data to generate this information. There are two ways to obtain these data:

Option 1: Download Preprocessed Human Pose Data

We have uploaded preprocessed human pose data for a subset of PandaSet scenes to Google Drive. You can download and unzip these files without installing any additional environment.

# https://drive.google.com/file/d/1ODzoH7SxNzjOThhKUc_n2LLXcOAeGMCM/view?usp=drive_link
# filename: pandaset_preprocess_humanpose.zip
cd data
gdown 1ODzoH7SxNzjOThhKUc_n2LLXcOAeGMCM

unzip pandaset_preprocess_humanpose.zip
rm pandaset_preprocess_humanpose.zip

Option 2: Run the Extraction Pipeline

To process human body poses for other PandaSet scenes or to run the processing pipeline yourself, follow the instructions in our Human Pose Processing Guide.

6. Data Structure

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

ProjectPath/data/
  └── pandaset/
    ├── raw/
    │    └── [original PandaSet structure]
    └── processed/
         ├── 001/
         │  ├──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
         ├── 002/
         └── ...