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second

Implementation of SECOND in PyTorch for KITTI 3D Object Detetcion

Acknowledgement

Installation

  • Clone this repository 
    git clone git@github.com:shangjie-li/second.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 second
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 second
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 
    second
├── 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 second/data/kitti.
cd second
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 second_7862.pth and save it in second/weights.) 
    # Run on the testing dataset
python demo.py --ckpt=weights/second_7862.pth

# Run on a single sample from the testing dataset
python demo.py --ckpt=weights/second_7862.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/second_7862.pth
  • The 3D detection performance on KITTI should be
AP (R11) BEV AP (R11) 3D
Car (Iou=0.7) 90.0097, 87.9282, 86.4528 88.6137, 78.6245, 77.2243
Pedestrian (Iou=0.5) 61.9979, 56.6604, 53.8126 56.5544, 52.9835, 47.7343
Cyclist (Iou=0.5) 84.0183, 70.7012, 65.4772 80.5862, 67.1589, 63.1087 
* Report in different difficulties, which are Easy, Moderate and Hard.