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PyTorch implementation of "HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection"

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This is the implementation of the paper "HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection (CVPR 2021)".

Our code is mainly based on OpenPCDet. We also plan to release the code based on PointPillars. For more information, checkout the project site [website] and the paper [PDF].

Dependencies

  • Python >= 3.6
  • PyTorch >= 1.4.0

Update

  • 20/06/21: First update

Installation

  • Clone this repo, and follow the steps below (or you can follow the installation steps in OpenPCDet).

  1. Clone this repository:

    git clone https://github.com/cvlab-yonsei/HVPR.git

  2. Install the dependent libraries:

    pip install -r requirements.txt

  3. Install the SparseConv library from spconv.

  4. Install pcdet library:

    python setup.py develop

Datasets

  • KITTI 3D Object Detection
  1. 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): 
    HVPR
├── data
│   ├── kitti
│   │   │── ImageSets
│   │   │── training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │── testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools
  2. 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

Training

  • The config files is in tools/cfgs/kitti_models, and you can easily train your own model like: 
    cd tools
sh scripts/train_hvpr.sh
  • You can freely define parameters with your own settings like: 
    cd tools
sh scripts train_hvpr.sh --gpus 1 --result_path 'your_dataset_directory' --exp_dir 'your_log_directory'

Evaluation

  • Test your own model: 
    cd tools
sh scripts/eval_hvpr.sh

Pre-trained model

Bibtex

@article{noh2021hvpr,
  title={HVPR: Hybrid Voxel-Point Representation for Single-stage 3D Object Detection},
  author={Noh, Jongyoun and Lee, Sanghoon and Ham, Bumsub},
  journal={arXiv preprint arXiv:2104.00902},
  year={2021}
}

References

Our work is mainly built on OpenPCDet codebase. Portions of our code are also borrowed from spconv, MemAE, and CBAM. Thanks to the authors!