/ Machine Learning

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds

Super-Fast-Accurate-3D-Object-Detection

Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds (The PyTorch implementation).

Features

  • [x] Super fast and accurate 3D object detection based on LiDAR
  • [x] Fast training, fast inference
  • [x] An Anchor-free approach
  • [x] No Non-Max-Suppression
  • [x] Support distributed data parallel training
  • [x] Release pre-trained models

Demonstration (on GTX 1080Ti)

2. Getting Started

2.1. Requirement

pip install -U -r requirements.txt

2.2. Data Preparation

Download the 3D KITTI detection dataset from here.

The downloaded data includes:

  • Velodyne point clouds (29 GB)
  • Training labels of object data set (5 MB)
  • Camera calibration matrices of object data set (16 MB)
  • Left color images of object data set (12 GB) (For visualization purpose only)

Please make sure that you construct the source code & dataset directories structure as below.

2.3. How to run

2.3.1. Visualize the dataset

To visualize 3D point clouds with 3D boxes, let's execute:

cd src/data_process
python kitti_dataset.py

2.3.2. Inference

The pre-trained model was pushed to this repo.

python test.py --gpu_idx 0 --peak_thresh 0.2

2.3.3. Making demonstration

python demo_2_sides.py --gpu_idx 0 --peak_thresh 0.2

The data for the demonstration will be automatically downloaded by executing the above command.

2.3.4. Training

2.3.4.1. Single machine, single gpu
python train.py --gpu_idx 0
2.3.4.2. Distributed Data Parallel Training
  • Single machine (node), multiple GPUs
python train.py --multiprocessing-distributed --world-size 1 --rank 0 --batch_size 64 --num_workers 8
  • Two machines (two nodes), multiple GPUs

    • First machine
    python train.py --dist-url 'tcp://IP_OF_NODE1:FREEPORT' --multiprocessing-distributed --world-size 2 --rank 0 --batch_size 64 --num_workers 8
    
    • Second machine
    python train.py --dist-url 'tcp://IP_OF_NODE2:FREEPORT' --multiprocessing-distributed --world-size 2 --rank 1 --batch_size 64 --num_workers 8
    

Tensorboard

  • To track the training progress, go to the logs/ folder and
cd logs/<saved_fn>/tensorboard/
tensorboard --logdir=./

Contact

If you think this work is useful, please give me a star!

If you find any errors or have any suggestions, please contact me (Email: [email protected]).

Thank you!

Citation

@misc{Super-Fast-Accurate-3D-Object-Detection-PyTorch,
  author =       {Nguyen Mau Dung},
  title =        {{Super-Fast-Accurate-3D-Object-Detection-PyTorch}},
  howpublished = {\url{https://github.com/maudzung/Super-Fast-Accurate-3D-Object-Detection}},
  year =         {2020}
}

References

[1] CenterNet: Objects as Points paper, PyTorch Implementation

[2] RTM3D: PyTorch Implementation

Folder structure

${ROOT}
└── checkpoints/
    ├── fpn_resnet_18/    
        ├── fpn_resnet_18_epoch_300.pth
└── dataset/    
    └── kitti/
        ├──ImageSets/
        │   ├── test.txt
        │   ├── train.txt
        │   └── val.txt
        ├── training/
        │   ├── image_2/ (left color camera)
        │   ├── calib/
        │   ├── label_2/
        │   └── velodyne/
        └── testing/  
        │   ├── image_2/ (left color camera)
        │   ├── calib/
        │   └── velodyne/
        └── classes_names.txt
└── src/
    ├── config/
    │   ├── train_config.py
    │   └── kitti_config.py
    ├── data_process/
    │   ├── kitti_dataloader.py
    │   ├── kitti_dataset.py
    │   └── kitti_data_utils.py
    ├── models/
    │   ├── fpn_resnet.py
    │   ├── resnet.py
    │   └── model_utils.py
    └── utils/
    │   ├── demo_utils.py
    │   ├── evaluation_utils.py
    │   ├── logger.py
    │   ├── misc.py
    │   ├── torch_utils.py
    │   ├── train_utils.py
    │   └── visualization_utils.py
    ├── demo_2_sides.py
    ├── demo_front.py
    ├── test.py
    └── train.py
├── README.md 
└── requirements.txt

GitHub

Comments