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MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving

CICAI 2023 Best Paper Runner-up Award

For business inquiries, please contact us at [email protected].

Please note that this is currently a pre-release version, several refactors will be made in the near future, which include removing the nerfstudio/ and adapting to PyTorch 2.0 & nerfstudio 0.3.x, etc. After that, this version will no longer be maintained.

1. Installation: Setup the environment

Prerequisites

You must have an NVIDIA video card with CUDA installed on the system. This library has been tested with version 11.3/11.7 of CUDA. You can find more information about installing CUDA here.

Create environment

Nerfstudio requires python >= 3.7. We recommend using conda to manage dependencies. Make sure to install Conda before proceeding.

conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
python -m pip install --upgrade pip
pip install --upgrade pip setuptools

Installation

This section will walk you through the installation process. Our system is dependent on the nerfstudio project.

  1. Install tiny-cuda-nn firstly.
  2. Install MARS locally with:
git clone [email protected]:OPEN-AIR-SUN/mars.git
cd mars/nerfstudio
pip install -e .[dev]         # install nerfstudio and its dependencies
cd ..
pip install -e .
ns-install-cli                # optional, only for tab completion

2. Training from Scratch

The following will train a MARS model.

Our repository provides dataparser for KITTI and vKITTI2 datasets, for your own data, you can write your own dataparser or convert your own dataset to the format of the provided datasets.

From Datasets

Data Preparation

The data used in our experiments should contain both the pose parameters of cameras and object tracklets. The camera parameters include the intrinsics and the extrinsic. The object tracklets include the bounding box poses, types, ids, etc. For more information, you can refer to KITTI-MOT or vKITTI2 datasets below.

KITTI

The KITTI-MOT dataset should look like this:

.(KITTI_MOT_ROOT)
├── panoptic_maps                    # (Optional) panoptic segmentation from KITTI-STEP dataset.
│   ├── colors
│   │   └── sequence_id.txt
│   ├── train
│   │   └── sequence_id
│   │       └── frame_id.png
└── training
    ├── calib
    │   └── sequence_id.txt
    ├── completion_02                # (Optional) depth completion
    │   └── sequence_id
    │       └── frame_id.png
    ├── completion_03
    │   └── sequence_id
    │       └── frame_id.png
    ├── image_02
    │   └── sequence_id
    │       └── frame_id.png
    ├── image_03
    │   └── sequence_id
    │       └── frame_id.png
    ├── label_02
    │   └── sequence_id.txt
    └── oxts
        └── sequence_id.txt

We download the KITTI-STEP annotations and generate the panoptic segmentation maps for KITTI-MOT dataset. You can download the demo panoptic maps here and put them in the KITTI-MOT directory, or you can visit the official website of KITTI-STEP for more information.

To train a reconstruction model, you can use the following command:

ns-train nsg-kitti-car-depth-recon --data /data/kitti-MOT/training/image_02/0006

or if you want to use the Python script (please refer to the launch.json file in the .vscode directory):

python nerfstudio/nerfstudio/scripts/train.py nsg-kitti-car-depth-recon --data /data/kitti-MOT/training/image_02/0006

vKITTI2

The vKITTI2 dataset should look like this:

.(vKITTI2_ROOT)
└── sequence_id
    └── scene_name
        ├── bbox.txt
        ├── colors.txt
        ├── extrinsic.txt
        ├── info.txt
        ├── instrinsic.txt
        ├── pose.txt
        └── frames
            ├── depth
            │   ├── Camera_0
            │   │   └── frame_id.png
            │   └── Camera_1
            │   │   └── frame_id.png
            ├── instanceSegmentation
            │   ├── Camera_0
            │   │   └── frame_id.png
            │   └── Camera_1
            │   │   └── frame_id.png
            ├── classSegmentation
            │   ├── Camera_0
            │   │   └── frame_id.png
            │   └── Camera_1
            │   │   └── frame_id.png
            └── rgb
                ├── Camera_0
                │   └── frame_id.png
                └── Camera_1
                    └── frame_id.png

To train a reconstruction model, you can use the following command:

ns-train nsg-vkitti-car-depth-recon --data /data/vkitti/Scene06/clone

or if you want to use the python script:

python nerfstudio/nerfstudio/scripts/train.py nsg-vkitti-car-depth-recon --data /data/vkitti/Scene06/clone

Your Own Data

For your own data, you can refer to the above data structure and write your own dataparser, or you can convert your own dataset to the format of the dataset above.

From Pre-Trained Model

Our model uses nerfstudio as the training framework, we provide the reconstruction and novel view synthesis tasks checkpoints.

Our pre-trained model is uploaded to Google Drive, you can refer to the below table to download the model.

Dataset Scene First Frame Last Frame Setting PSNR SSIM Download
KITTI-MOT 0006 5 260 Reconstruction 29.06 0.885 model
0006 5 260 Novel View Synthesis 75% 24.23 0.845 model
0006 5 260 Novel View Synthesis 50% 24.00 0.801 model
0006 5 260 Novel View Synthesis 25% 23.23 0.756 model
Vitural KITTI-2 Scene06 0 237 Novel View Synthesis 75% 29.79 0.917 model
Scene06 0 237 Novel View Synthesis 50% 29.63 0.916 model
Scene06 0 237 Novel View Synthesis 25% 27.01 0.887 model

You can use the following command to train a model from a pre-trained model:

ns-train nsg-kitti-car-depth-recon --data /data/kitti-MOT/training/image_02/0006 --load-dir outputs/experiment_name/method_name/timestamp/nerfstudio

Model Configs

Our modular framework supports combining different architectures for each node by modifying model configurations. Here's an example of using Nerfacto for background and our category-level object model:

model=SceneGraphModelConfig(
    background_model=NerfactoModelConfig(),
    object_model_template=CarNeRFModelConfig(_target=CarNeRF),
    object_representation="class-wise",
    object_ray_sample_strategy="remove-bg",
)

If you choose to use the category-level object model, please make sure that the use_car_latents=True and the latent codes exists. We provide latent codes of some sequences on KITTI-MOT and vKITTI2 datasets here.

For more information, please refer to our provided configurations at nsg/cicai_configs.py. We use wandb for logging by default, you can also specify other viewers (tensorboard/nerfstudio-viewer supported) with the --vis config. Please refer to the nerfstudio documentation for details.

Render

If you want to render with our pre-trained model, you should visit here to download our checkpoints and config. To run the render script, you need to ensure that your config is the same as the config.yml that you load in.

You can use the following command to render:

python scripts/cicai_render.py --load-config outputs/nvs75fullseq/nsg-vkitti-car-depth-nvs/2023-06-21_135412/config.yml --output-path renders/

Citation

You can find our paper here. If you use this library or find the repo useful for your research, please consider citing:

@article{wu2023mars,
  author    = {Wu, Zirui and Liu, Tianyu and Luo, Liyi and Zhong, Zhide and Chen, Jianteng and Xiao, Hongmin and Hou, Chao and Lou, Haozhe and Chen, Yuantao and Yang, Runyi and Huang, Yuxin and Ye, Xiaoyu and Yan, Zike and Shi, Yongliang and Liao, Yiyi and Zhao, Hao},
  title     = {MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving},
  journal   = {CICAI},
  year      = {2023},
}

Acknoledgement

Part of our code is borrowed from Nerfstudio. This project is sponsored by Tsinghua-Toyota Joint Research Fund (20223930097) and Baidu Inc. through Apollo-AIR Joint Research Center.

Notice

This open-sourced version will be actively maintained and regularly updated. For more features, please contact us for a commercial version.