Python-MonoGRNet一种用于单目三维物体检测和定位的几何推理网络KITTI

## MonoGRNet: A Geometric Reasoning Network for 3D Object Localization [](https://cloud.tsinghua.edu.cn/f/194ddabfd05d4dc78b9f/) #### The repository contains an implementation of this [AAAI Oral Paper](https://arxiv.org/abs/1811.10247). #### [Video Demo](https://cloud.tsinghua.edu.cn/f/194ddabfd05d4dc78b9f/) #### [Detection Outputs on KITTI Validation Set](https://cloud.tsinghua.edu.cn/f/57dbe05ad60b4e3c8e64/?dl=1) <br/> ### Related Project [**Triangulation Learning Network: from Monocular to Stereo 3D Object Detection**](https://github.com/Zengyi-Qin/TLNet) Please cite this paper if you find the repository helpful: ``` @article{qin2019monogrnet, title={MonoGRNet: A Geometric Reasoning Network for 3D Object Localization}, author={Zengyi Qin and Jinglu Wang and Yan Lu}, journal={The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)}, year={2019} } ``` ### Prerequisites - Ubuntu 16.04 - Python 2.7 - Tensorflow 1.4.0 ### Install Clone this repository ```bash git clone https://github.com/Zengyi-Qin/MonoGRNet.git ``` Download the [Kitti Object Detection Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d) ([image](http://www.cvlibs.net/download.php?file=data_object_image_2.zip), [calib](http://www.cvlibs.net/download.php?file=data_object_calib.zip) and [label](http://www.cvlibs.net/download.php?file=data_object_label_2.zip)) and place it into `data/KittiBox`. The folder should be in the following structure: ``` data KittiBox training calib image_2 label_2 train.txt val.txt ``` The train-val split `train.txt` and `val.txt` are contained in this repository. Compile the Cython module and download the pretrained model: ```bash python setup.py ``` ### Training and evaluation Run the training script and specify the GPU to use: ```bash python train.py --gpus 0 ``` The evaluation is done during training. You can adjust the evaluation intervals in `hypes/kittiBox.json`. ### Visualization ```bash cd visualize && mkdir visualize python visualize.py ``` ### Acknowledgement We would like to thank the authors of [KittiBox](https://github.com/MarvinTeichmann/KittiBox) for their code.