Dubhe/point-cloud/README.md

• Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds
• • 1. Getting Started
  • • 1.1 Requirement
    • 1.2 Data Preparation
  • 2. How to run
  • • 2.1 Inference
    • 2.2 Training
    • • 2.2.1 CPU
      • 2.2.2 Single machine, single gpu
      • 2.2.3 Distributed Data Parallel Training
  • References
  • Folder structure
  • • Dataset
    • Checkpoints & Algorithm

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

---

1. Getting Started

1.1 Requirement

The instructions for setting up a virtual environment is here.

cd SFA3D/
pip install -r requirements.txt

1.2 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)

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

2. How to run

2.1 Inference

The pre-trained model was pushed to this repo.

• CPU

python inference.py --no_cuda=True

• GPU

python inference.py

Label of inference

• Pedestrian
• Car
• Cyclist

2.2 Training

2.2.1 CPU

python train.py --no_cuda=True

2.2.2 Single machine, single gpu

python train.py --gpu_idx 0

2.2.3 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
  

References

[1] SFA3D: PyTorch Implementation

Folder structure

Dataset

└── kitti/    
     ├── image_2/ (left color camera，非必须)
     ├── calib/ (非必须)
     ├── label_2/ (标注结果/标签，非必须)
     └── velodyne/ (点云文件，必须) 

Checkpoints & Algorithm

${ROOT}
└── checkpoints/
    ├── fpn_resnet_18/    
        ├── fpn_resnet_18_epoch_300.pth (点云目标检测标注模型)     
└── sfa/ (点云标注算法)
    ├── config/
    ├── data_process/
    ├── models/
    ├── utils/
    ├── inference.py
    └── train.py
├── README.md 
├── LICENSE
└── requirements.txt