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https://open-air-sun.github.io/mars/
MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
https://open-air-sun.github.io/mars/
Last synced: 3 months ago
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MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving
- Host: GitHub
- URL: https://open-air-sun.github.io/mars/
- Owner: OPEN-AIR-SUN
- License: apache-2.0
- Created: 2023-07-17T05:34:36.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-06-10T19:02:09.000Z (7 months ago)
- Last Synced: 2024-10-02T08:13:44.257Z (3 months ago)
- Language: Python
- Homepage:
- Size: 110 MB
- Stars: 673
- Watchers: 11
- Forks: 64
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scene-representation - MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving - AIR-SUN/mars) | [bibtex](./citations/wu2023mars.txt) (Uncategorized / Uncategorized)
README
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].
> We have just finished a refactorization of our codebase. Now you can use `pip install` to start using mars instantly! Please contact us without hesitation if you encounter any issues using the latest version. Thanks!
> Our related(dependent) project -- CarStudio is accepted by RAL and their code is available at [GitHub](https://github.com/lty2226262/Car_studio). Congrats to them!
## 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 11.7 of CUDA. You can find more information about installing CUDA [here](https://docs.nvidia.com/cuda/cuda-quick-start-guide/index.html).
#### Create environment
Nerfstudio requires `python >= 3.7`. We recommend using conda to manage dependencies. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html) before proceeding.
```bash
conda create --name mars -y python=3.9
conda activate mars
```
#### Installation
This section will walk you through the installation process. Our system is dependent on the tiny-cuda-nn project.
```bash
pip install mars-nerfstudio
cd /path/to/tiny-cuda-nn/bindings/torch
python setup.py install
```
## 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 extrinsics. 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](https://www.cvlibs.net/datasets/kitti/eval_tracking.php) 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 use a [monocular depth estimation model](https://github.com/theNded/mini-omnidata) to generate the depth maps for KITTI-MOT dataset. [Here](https://drive.google.com/drive/folders/1Y-41OMCzDkdJ2P-YZHtCI-5YR9jAIKS2?usp=drive_link) is the estimation result of 0006 sequence of KITTI-MOT datasets. You can download and put them in the `KITTI-MOT/training` directory.
> We download the KITTI-STEP annotations and generate the panoptic segmentation maps for KITTI-MOT dataset. You can download the demo panoptic maps [here](https://drive.google.com/drive/folders/1obAyq1jlHbyA9CS9Rg66N3YyI_sjpfGB?usp=drive_link) and put them in the `KITTI-MOT` directory, or you can visit the official website of [KITTI-STEP](https://www.cvlibs.net/datasets/kitti/eval_step.php) for more information.
To train a reconstruction model, you can use the following command:
```bash
ns-train mars-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):
```bash
python nerfstudio/nerfstudio/scripts/train.py mars-kitti-car-depth-recon --data /data/kitti-MOT/training/image_02/0006
```
#### vKITTI2
The [vKITTI2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/) 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:
```bash
ns-train mars-vkitti-car-depth-recon --data /data/vkitti/Scene06/clone
```
or if you want to use the python script:
```bash
python nerfstudio/nerfstudio/scripts/train.py mars-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
Setting
Start-End
Steps
PSNR
SSIM
Download
Wandb
KITTI-MOT
0006
Reconstruction
65-120
400k
27.96
0.900
model
report
0006
Novel View Synthesis 75%
65-120
200k
27.32
0.890
model
report
0006
Novel View Synthesis 50%
65-120
200k
26.80
0.883
model
report
0006
Novel View Synthesis 25%
65-120
200k
25.87
0.866
model
report
Vitural KITTI-2
Scene06
Novel View Synthesis 75%
0-237
600k
32.32
0.940
model
report
Scene06
Novel View Synthesis 50%
0-237
600k
32.16
0.938
model
report
Scene06
Novel View Synthesis 25%
0-237
600k
30.87
0.935
model
report
You can use the following command to train a model from a pre-trained model:
```bash
ns-train mars-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:
```python
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](https://drive.google.com/drive/folders/1E4YjMwkDbRsF4Hb1UK0iBDkz-tFVx3Me?usp=sharing).
For more information, please refer to our provided configurations at `mars/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](https://drive.google.com/drive/folders/1-5NR2n3o5zD9ViWwovATOshKYfH3us1I?usp=drive_link) 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. You can modify output format and directory by specificing `--output-format` and `--output-path` :
```bash
python scripts/cicai_render.py --load-config outputs/kitti-recon-65-120/mars-kitti-car-depth-recon/2023-10-30_212654/config.yml --output-format video
or
python scripts/cicai_render.py --load-config outputs/kitti-recon-65-120/mars-kitti-car-depth-recon/2023-10-30_212654/config.yml --output-format images --output-path /path/to/your/output/directory
```
## Citation
You can find our paper [here](https://open-air-sun.github.io/mars/static/data/CICAI_MARS_FullPaper.pdf). 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](https://nerf.studio). 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.