Implementation of PointPillars in PyTorch for KITTI 3D Object Detetcion
- This repository references open-mmlab's work.
- Clone this repository
git clone [email protected]: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
- 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 # Display the testing dataset python dataset_player.py
- Run the demo with a pretrained model (Download pointpillar_7728.pth and save it in pointpillars/weights.)
# Run on a single file python demo.py --ckpt=weights/pointpillar_7728.pth --data_path=data/kitti/training/velodyne/000008.bin # Run on a folder python demo.py --ckpt=weights/pointpillar_7728.pth --data_path=data/kitti/training/velodyne
- Run the command below to train
python train.py --batch_size=2
- Run the command below to evaluate
python test.py --ckpt=weights/pointpillar_7728.pth
- The 3D detection performance on KITTI should be
Class AP (R11) BEV AP (R11) 3D Car (Iou=0.7) 89.6590, 87.1725, 84.3762 86.4617, 77.2839, 74.6530 Pedestrian (Iou=0.5) 61.6348, 56.2747, 52.6007 57.7500, 52.2916, 47.9072 Cyclist (Iou=0.5) 82.2593, 66.1110, 62.5585 80.0483, 62.6080, 59.5260 - Report in different difficulties, which are Easy, Moderate and Hard.