WYSIWYD

The official code implementation for the paper "What You See Is What You Detect: Towards Better Object Densification in 3D Detection". We first released the augmented lidar signal for performance open review. The codes to generate added points will be released after the paper gets accepted.

We now provide pre-trained weights for Voxel-RCNN and the augmented lidar points.

Our paper is available on ArXiv at https://arxiv.org/abs/2310.17842

Results reproduce

1. Prepare for the running environment.

   Please follow the docker image provided by Voxel-R-CNN to set up your environment.

2. Replace the configuration files provided

   Please download the related weights and files from the following links.

   • Baidu Cloud
     or
   • OneDrive
     You need to change the 'get_lidar' function in pcdet/datasets/kitti/kitti_dataset.py around line 70 by

   lidar_file = self.root_split_path / 'velodyne_fop_aug' / 'velodyne_fop_only_car_ped_-1' / ('%s.bin' % idx)
   

3. Your dataset dir should look like follows:

      WYSIWYD
      ├── data
      │   ├── kitti
      │   │   │── ImageSets
      │   │   │── training
      │   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes) & velodyne_fop_only_car_ped_-1
      │   │   │── testing
      │   │   │   ├──calib & velodyne & image_2
      │   │   │── gt_database
      │   │   │── kitti_infos_test.pkl
      │   │   │── kitti_infos_train.pkl
      │   │   │── kitti_infos_trainval.pkl
      │   │   │── kitti_infos_val.pkl
      ├── pcdet
      ├── tools
     ```
   

4. Inference specific weights

   python tools/test.py --cfg_file /path_to_yaml_file/voxel_rcnn_3classes.yaml
   --batch_size 8
   --workers 8
   --ckpt /path_to_weight/main_res.pth
   

5. Train the model with the provided lidar data

   Please take the YAML file provided for an inference, cancel the GT-Sampling in the training pipeline, and will be better for performance.

With the main_res.pth, the result should be as follows. Please feel free to check the further results from our paper.

Car AP_R40@0.70, 0.70, 0.70:
bbox AP:98.9053, 95.9023, 93.4399
bev  AP:95.6424, 91.8239, 89.5842
3d   AP:92.6015, 85.8442, 83.4265
aos  AP:98.87, 95.71, 93.15

Pedestrian AP_R40@0.50, 0.50, 0.50:
bbox AP:80.4118, 78.1586, 71.7518
bev  AP:80.0973, 75.2514, 68.6998
3d   AP:75.5632, 69.3826, 64.5597
aos  AP:37.56, 37.52, 34.45

Acknowledge

Our codes are based on OpenPCDet and some parts of the code were designed with reference to Pixel2Mesh