SGF3D : Similarity-guided fusion network for 3D object detection

This is the official implementation of SGF3D: Similarity-guided fusion network for 3D object detection

Installation

1. Prepare for the running environment.

   You can refer to the OpenPCDet to prepare the training environment.

2. Prepare for the data.

   Please prepare KITTI dataset as OpenPCDet. Then download the generated pseudo point cloud from Baidu. Finally, your data should be prepared as follows:

   SGF3D
   ├── data
   │   ├── kitti_sgf_seguv_twise
   │   │   │── ImageSets
   │   │   │── training
   │   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & depth_dense_twise & depth_pseudo_rgbseguv_twise
   │   │   │── testing
   │   │   │   ├──calib & velodyne & image_2 & depth_dense_twise & depth_pseudo_rgbseguv_twise
   │   │   │── gt_database
   │   │   │── gt_database_pseudo_seguv
   │   │   │── kitti_dbinfos_train_sgf_seguv.pkl
   │   │   │── kitti_infos_test.pkl
   │   │   │── kitti_infos_train.pkl
   │   │   │── kitti_infos_trainval.pkl
   │   │   │── kitti_infos_val.pkl
   ├── pcdet
   ├── tools
   

You can also generate pseudo point cloud by yourself:

 cd SGF3D
 python depth_to_lidar.py

Well-trained weights

You can download our well-trained weights from Baidu

Getting Started

1. Training.

   cd SGF3D/tools
   python train.py --cfg_file cfgs/kitti_models/sgf.yaml
   

2. Evaluation.

   cd SGF3D/tools
   python test.py --cfg_file cfgs/kitti_models/sgf.yaml --ckpt checkpoint_epoch_66.pth
   

3. Visualization.

   You can refer to 3D-Detection-Tracking-Viewer or 3d-object-vis for visualization. Both of these visualization tools are based on vedo. You can also use Open3D or mayavi for visualization.