Super Fast and Accurate 3D Object Detection based on LiDAR

Features

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

Technical details could be found here

Demonstration

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

2.3.2. Making demonstration

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

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

2.3.3. Training

2.3.3.1. Single machine, single gpu

python train.py --gpu_idx 0

Tensorboard

To track the training progress, go to the logs/ folder and

cd logs/<saved_fn>/tensorboard/
tensorboard --logdir=./

Then go to http://localhost:6006/

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

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