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