Hallucinated Hollow-3D R-CNN

This is the official implementation of From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection, built on OpenPCDet. This paper has been accepted by IEEE TCSVT.

@article{deng2021hh3d,
  title={From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection},
  author={Deng, Jiajun and Zhou, Wengang and Zhang, Yanyong and Li, Houqiang},
  journal={arXiv:2107.14391},
  year={2021}
}

Installation

Prepare for the running environment.

You can either use the docker image we provide, or follow the installation steps in OpenPCDet.
```
docker pull djiajun1206/pcdet:pytorch1.6
```

Prepare for the data.

Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from [road plane], which are optional for data augmentation in the training):

Voxel-R-CNN
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools

Generate the data infos by running the following command:

python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml

Setup.
```
python setup.py develop
```

Getting Started

Downloading the model.

The model reported in the manuscript can be download here.
Training.

The configuration file is in tools/cfgs/kitti_models/hh3d_rcnn_car.yaml, and the training scripts is in tools/scripts.
```
cd tools
sh scripts/train_hh3d_rcnn.sh
```
Evaluation.

The configuration file is in tools/cfgs/voxelrcnn, and the training scripts is in tools/scripts.
```
cd tools
sh scripts/eval_hh3d_rcnn.sh
```

Acknowledge

Thanks to the strong and flexible OpenPCDet codebase.

djiajunustc/H-23D_R-CNN

Hallucinated Hollow-3D R-CNN

Installation

Getting Started

Acknowledge