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Installation

Requirements

All the codes are tested in the following environment:

  • Linux (tested on Ubuntu 16.04)
  • Python 3.6+
  • PyTorch 1.1 or higher (tested on PyTorch 1.5)
  • CUDA 9.0 or higher
  • spconv v1.0 (commit 8da6f96)

Install pcdet

a. Go to the OpenPCDet repository.

cd OpenPCDet/

b. Install the dependant libraries as follows:

  • Install the dependant python libraries:
pip install -r requirements.txt
  • Install the SparseConv library, we use the non-official implementation from spconv. Note that we use the initial version of spconv, make sure you install the spconv v1.0 (commit 8da6f96) instead of the latest one.
  • For docker image, please refer to:
docker pull trn84/repo:latest

c. Install this pcdet library by running the following command:

python setup.py develop

Dataset Preparation for KITTI

  • 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):
PCDet
├── 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