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A 3DGS framework for omni urban scene reconstruction and simulation!

About

DriveStudio is a 3DGS codebase for urban scene reconstruction/simulation. It offers a system with multiple Gaussian representations to jointly reconstruct backgrounds, vehicles, and non-rigid categories (pedestrians, cyclists, etc.) from driving logs. DriveStudio also provides a unified data system supporting various popular driving datasets, including Waymo, PandaSet, Argoverse2, KITTI, NuScenes, and NuPlan.

This codebase also contains the official implementation of:

OmniRe: Omni Urban Scene Reconstruction
Project Page | Paper
Ziyu Chen, Jiawei Yang, Jiahui Huang, Riccardo de Lutio, Janick Martinez Esturo, Boris Ivanovic, Or Litany, Zan Gojcic, Sanja Fidler, Marco Pavone, Li Song, Yue Wang

🎉 Try your own projects/research on DriveStudio!

🔥 Highlighted implementations

Our codebase supports two types of Gaussian trainers:

Single-Representation trainer (single Gaussian representation for the entire scene):
- Deformable Gaussians
- Periodic Vibration Gaussians
Multi-Representation trainer (Gaussian scene graphs trainer):
- Background: Static Gaussians (Vanilla Gaussians)
- Vehicles: Static Gaussians
- Humans: SMPL-Gaussians, Deformable Gaussians
- Other non-rigid categories: Deformable Gaussians

Implemented methods:

Method Name	Implementation	Trainer Type	Gaussian Representations
OmniRe	Official	Multi	• Static Gaussians: Background, Vehicles • SMPL Gaussians: Pedestrians (majority) • Deformable Gaussians: Cyclists, far-range pedestrians, other non-rigid categories
Deformable-GS	Unofficial	Single	• Deformable Gaussians: Entire scene
PVG	Unofficial	Single	• Periodic Vibration Gaussians: Entire scene
Street Gaussians	Unofficial	Multi	• Static Gaussians: Background, Vehicles

We extend our gratitude to the authors for their remarkable contributions. If you find these works useful, please consider citing them.

🚗 Dataset Support

This codebase provides support for popular driving datasets. We offer instructions and scripts on how to download and process these datasets:

Dataset	Instruction	Cameras	Sync Frequency	Object Annotation
Waymo	Data Process Instruction	5 cameras	10Hz	✅
NuScenes	Data Process Instruction	6 cameras	2Hz (up to 10Hz*)	✅
NuPlan	Data Process Instruction	8 cameras	10Hz	✅
ArgoVerse	Data Process Instruction	7 cameras	10Hz	✅
PandaSet	Data Process Instruction	6 cameras	10Hz	✅
KITTI	Data Process Instruction	2 cameras	10Hz	✅

*NOTE: For NuScenes data, LiDAR operates at 20Hz and cameras at 12Hz, but keyframes (with object annotations) are only at 2Hz. We provide a method to interpolate annotations up to 10Hz.

✨ Functionality

Click to expand functionality details

We have implemented interesting and useful functionalities:

Flexible multi-camera training: Choose any combination of cameras for training - single, multiple, or all. You can set these up by SIMPLY configuring your selection in the config file.
Powered by gsplat Integrated gsplat rasterization kernel with its advanced functions, e.g. absolute gradients, anti-aliasing, etc.
Camera Pose Refinement: Recognizing that camera poses may not always be sufficiently accurate, we provide a method to refine and optimize these poses.
Objects' GT Bounding Box Refinement: To address noise in ground truth boxes, we've added this feature to further improve accuracy and robustness.
Affine Transformation: This feature handles camera exposure and other related issues, enhancing the quality of scene reconstruction.
...

These functionalities are designed to enhance the overall performance and flexibility of our system, allowing for more accurate and adaptable scene reconstruction across various datasets and conditions.

📢 Updates

[Aug 2024] Release code of DriveStudio.

🔨 Installation

Run the following commands to set up the environment:

# Clone the repository with submodules
git clone --recursive https://github.com/ziyc/drivestudio.git
cd drivestudio

# Create the environment
conda create -n drivestudio python=3.9 -y
conda activate drivestudio
pip install -r requirements.txt
pip install git+https://github.com/facebookresearch/pytorch3d.git
pip install git+https://github.com/NVlabs/nvdiffrast

# Set up for SMPL Gaussians
cd third_party/smplx/
pip install -e .
cd ../..

📊 Prepare Data

We support most popular public driving datasets. Detailed instructions for downloading and processing each dataset are available in the following documents:

🚀 Running

Training

export PYTHONPATH=$(pwd)

python tools/train.py \
    --config_file configs/omnire.yaml \
    --output_root $output_root \
    --project $project \
    --run_name $expname \
    dataset=waymo/3cams \
    data.scene_idx=$scene_idx \
    data.start_timestep=$start_timestep \  # start frame index for training
    data.end_timestep=$end_timestep  # end frame index, -1 for the last frame

To run other methods, change --config_file. See configs/ for more options.
Specify dataset and number of cameras by setting dataset. Examples: waymo/1cams, waymo/5cams, pandaset/6cams, argoverse/7cams, etc. You can set up arbitrary camera combinations for each dataset. See configs/datasets/ for custom configuration details.
For over 3 cameras or 450+ images, we recommend using omnire_extended_cam.yaml. It works better in practice.

Evaluation

python tools/eval.py --resume_from $ckpt_path

👏 Contributions

We're improving our project to develop a robust driving recom/sim system. Some areas we're focusing on:

A real-time viewer for background and foreground visualization
Scene editing and simulation tools
Other Gaussian representations (e.g., 2DGS, surfels)

We welcome pull requests and collaborations. If you'd like to contribute or have questions, feel free to open an issue or contact Ziyu Chen (ziyu.sjtu@gmail.com).

🙏 Acknowledgments

We utilize the rasterization kernel from gsplat. Parts of our implementation are based on work from EmerNeRF, NerfStudio, GART, and Neuralsim. We've also implemented unofficial versions of Deformable-GS, PVG, and Street Gaussians, with reference to their original codebases.

We extend our deepest gratitude to the authors for their contributions to the community, which have greatly supported our research.

Citation

@article{chen2024omnire,
    title={OmniRe: Omni Urban Scene Reconstruction},
    author={Chen, Ziyu and Yang, Jiawei and Huang, Jiahui and Lutio, Riccardo de and Esturo, Janick Martinez and Ivanovic, Boris and Litany, Or and Gojcic, Zan and Fidler, Sanja and Pavone, Marco and Song, Li and Wang, Yue},
    journal={arXiv preprint arXiv:2408.16760},
    year={2024}
}

areslp/drivestudio