3D目标检测-基于点云的弱监督三维目标检测算法实现-附项目源码-优质项目实战.zip

  Enter the main folder and run installation: ```bash pip install -r requirements.txt ``` Download the [demo data](https://drive.google.com/file/d/1s4G3avlud7H4oqEBpi0GMnL20HjPJ9Wd/view?usp=sharing) to the main folder and run `unzip vs3d_demo.zip`. Readers can try out the quick demo with Jupyter Notebook: ```bash cd core jupyter notebook demo.ipynb ``` ### Training 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 them into `data/kitti`. Download the ground planes and front-view XYZ maps from [here](https://drive.google.com/file/d/1SGgIFLK8t7IVf6Cb_yk83NGWzJcx5WwK/view?usp=sharing) and run `unzip vs3d_train.zip`. Download the pretrained teacher network from [here](https://drive.google.com/file/d/1rnTjOg7R8Ino6778OPTxJSvHBzuc50Yv/view?usp=sharing) and run `unzip vs3d_pretrained.zip`. The data folder should be in the following structure: ``` ├── data │ ├── demo │ └── kitti │ └── training │ ├── calib │ ├── image_2 │ ├── label_2 │ ├── sphere │ ├── planes │ └── velodyne │ ├── train.txt │ └── val.txt │ └── pretrained │ ├── student │ └── teacher ``` The `sphere` folder contains the front-view XYZ maps converted from `velodyne` point clouds using the script in `./preprocess/sphere_map.py`. After data preparation, readers can train VS3D from scratch by running: ```bash cd core python main.py --mode train --gpu GPU_ID ``` The models are saved in `./core/runs/weights` during training. Reader can refer to `./core/main.py` for other options in training. ### Inference Readers can run the inference on KITTI validation set by running: ```bash cd core python main.py --mode evaluate --gpu GPU_ID --student_model SAVED_MODEL ``` Readers can also directly use the pretrained model for inference by passing `--student_model ../data/pretrained/student/model_lidar_158000`. Predicted 3D bounding boxes are saved in `./output/bbox` in KITTI format.