激光雷达雾模拟___下载.zip

Created by [Martin Hahner](https://sites.google.com/view/martinhahner/home) at the [Computer Vision Lab](https://vision.ee.ethz.ch/) of [ETH Zurich](https://ethz.ch/). [](https://arxiv.org/abs/2108.05249) [](https://paperswithcode.com/sota/3d-object-detection-on-stf?p=fog-simulation-on-real-lidar-point-clouds-for)  # [Fog Simulation on Real LiDAR Point Clouds <br> for 3D Object Detection in Adverse Weather](https://arxiv.org/abs/2108.05249) *by [Martin Hahner](https://www.trace.ethz.ch/team/members/martin.html), [Christos Sakaridis](https://www.trace.ethz.ch/team/members/christos.html), [Dengxin Dai](https://www.trace.ethz.ch/team/members/dengxin.html), and [Luc van Gool](https://www.trace.ethz.ch/team/members/luc.html)* Accepted at [ICCV 2021](http://iccv2021.thecvf.com). <br> Please visit our [paper website](https://trace.ethz.ch/lidar_fog_sim) for more details.  ## Overview . ├── file_lists # contains file lists for pointcloud_viewer.py │ └── ... ├── integral_lookup_tables # contains lookup tables to speed up the fog simulation │ └── ... ├── extract_fog.py # to extract real fog noise* from the SeeingThroughFog dataset ├── fog_simulation.py # to augment a clear weather pointcloud with artificial fog (used during training) ├── generate_integral_lookup_table.py # to precompute the integral inside the fog equation ├── pointcloud_viewer.py # to visualize entire point clouds of different datasets with the option to augment fog into their scenes ├── README.md └── theory.py # to visualize the theory behind a single LiDAR beam in foggy conditions \* Contains returns not only from fog, but also from physical objects that are closeby. **Datasets supported by `pointcloud_viewer.py`:** - [H3D](https://usa.honda-ri.com/H3D) - [A2D2](https://www.a2d2.audi/a2d2/en.html) - [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) - [LyftL5](https://self-driving.lyft.com/level5/prediction/) - [Pandaset](https://pandaset.org/) - [nuScenes](https://www.nuscenes.org/nuscenes) - [Argoverse](https://www.argoverse.org/data.html#tracking-link) - [ApolloScape](http://apolloscape.auto/tracking.html) - **[SeeingThroughFog](https://www.cs.princeton.edu/~fheide/AdverseWeatherFusion/)** &nbsp;:arrow_left: works best - [WaymoOpenDataset](https://waymo.com/open/) (via [waymo_kitti_converter](https://github.com/caizhongang/waymo_kitti_converter)) ## License This software is made available for non-commercial use under a Creative Commons [License](LICENSE).<br> A summary of the license can be found [here](https://creativecommons.org/licenses/by-nc/4.0/). ## Acknowledgments This work is supported by [Toyota](https://www.toyota-europe.com/) via the [TRACE](https://www.trace.ethz.ch/) project. Furthermore, we would like to thank the authors of [SeeingThroughFog](https://www.cs.princeton.edu/~fheide/AdverseWeatherFusion/) for their great work. <br> In this repository, we use a [fork](https://github.com/MartinHahner/SeeingThroughFog) of [their original repository](https://github.com/princeton-computational-imaging/SeeingThroughFog) to visualize annotations and compare to their fog simulation. Their code is licensed via the [MIT License](https://github.com/princeton-computational-imaging/SeeingThroughFog/blob/master/LICENSE). ## Citation(s) If you find this work useful, please consider citing our paper. ```bibtex @inproceedings{HahnerICCV21, author = {Hahner, Martin and Sakaridis, Christos and Dai, Dengxin and Van Gool, Luc}, title = {{Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather}}, booktitle = {IEEE International Conference on Computer Vision (ICCV)}, year = {2021}, } ``` You may also want to check out our latest work (Oral at CVPR 2022)<br> [*LiDAR Snowfall Simulation for Robust 3D Object Detection*](https://github.com/SysCV/LiDAR_snow_sim). ```bibtex @inproceedings{HahnerCVPR22, author = {Hahner, Martin and Sakaridis, Christos and Bijelic, Mario and Heide, Felix and Yu, Fisher and Dai, Dengxin and Van Gool, Luc}, title = {{LiDAR Snowfall Simulation for Robust 3D Object Detection}}, booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2022}, } ``` ## Getting Started ### Setup 1) Install [anaconda](https://docs.anaconda.com/anaconda/install/). 2) Create a new conda environment. ```bash conda create --name foggy_lidar python=3.9 -y ``` 3) Activate the newly created conda environment. ```bash conda activate foggy_lidar ``` 4) Install all necessary packages. ```bash conda install matplotlib numpy opencv pandas plyfile pyopengl pyqt pyqtgraph quaternion scipy tqdm -c conda-forge -y pip install pyquaternion ``` 5) Clone this repository (including submodules). ```bash git clone [email protected]:MartinHahner/LiDAR_fog_sim.git --recursive cd LiDAR_fog_sim ``` ### Usage How to run the script that visualizes the theory behind a single LiDAR beam in foggy conditions: ```bash python theory.py ```  How to run the script that visualizes entire point clouds of different datasets: ```bash python pointcloud_viewer.py -d <path_to_where_you_store_your_datasets> ``` **Note:** You may also have to adjust the relative paths in `pointcloud_viewer.py` (right at the beginning of the file) to be compatible with your datasets relative folder structure. ### Disclaimer The code has been successfully tested on - Ubuntu 18.04.5 LTS - macOS Big Sur 11.2.1 - Debian GNU/Linux 9.13 using conda 4.9.2. ## Contributions Please feel free to suggest improvements to this repository.<br> We are always open to merge usefull pull request.