pointpillars

Implementation of PointPillars in PyTorch for KITTI 3D Object Detetcion

Acknowledgement

• This repository references open-mmlab's work.

Installation

• Clone this repository

  git clone git@github.com:shangjie-li/pointpillars.git

• Install PyTorch environment with Anaconda (Tested on Ubuntu 16.04 & CUDA 10.2)

  conda create -n pcdet.v0.5.0 python=3.6
  conda activate pcdet.v0.5.0
  cd pointpillars
  pip install -r requirements.txt

• Install spconv

  # Try the command below:
  pip install spconv-cu102
  
  # If there is `ERROR: Cannot uninstall 'certifi'.`, try:
  pip install spconv-cu102 --ignore-installed

• Compile external modules

  cd pointpillars
  python setup.py develop

• Install visualization tools

  pip install mayavi
  pip install pyqt5
  
  # If you want import opencv, run:
  pip install opencv-python
  
  # If you want import open3d, run:
  pip install open3d-python

KITTI3D Dataset (41.5GB)

• Download KITTI3D dataset: calib, velodyne, label_2 and image_2.
• Download road plane for data augmentation.
• Organize the downloaded files as follows

  pointpillars
  ├── data
  │   ├── kitti
  │   │   │── ImageSets
  │   │   │── training
  │   │   │   ├──calib & velodyne & label_2 & image_2 & planes
  │   │   │── testing
  │   │   │   ├──calib & velodyne & image_2
  ├── layers
  ├── utils

• Generate the ground truth database and data infos by running the following command

  # This will create gt_database dir and info files in pointpillars/data/kitti.
  cd pointpillars
  python -m data.kitti_dataset create_kitti_infos data/config.yaml

• Display the dataset

  # Display the training dataset with data augmentation
  python dataset_player.py --training --data_augmentation --show_boxes
  
  # Display the testing dataset
  python dataset_player.py --show_boxes

Demo

• Run the demo with a pretrained model (Download pointpillar_7728.pth and save it in pointpillars/weights.)

  # Run on the testing dataset
  python demo.py --ckpt=weights/pointpillar_7728.pth
  
  # Run on a single sample from the testing dataset
  python demo.py --ckpt=weights/pointpillar_7728.pth --sample_idx=000008

Training

• Run the command below to train

  python train.py --batch_size=2

Evaluation

• Run the command below to evaluate

  python test.py --ckpt=weights/pointpillar_7728.pth

• The 3D detection performance on KITTI should be

* Report in different difficulties, which are Easy, Moderate and Hard.