前言
Amusi 发现了一个超快速3D目标检测网络！SFA3D：基于LiDAR的实时、准确的3D目标检测模型，在GTX 1080 Ti上速度高达95 FPS！代码现已开源！
主要特性：
1. 快速训练和推理；
2. Anchor-free的方法；
3. 无NMS；
4. 支持分布式数据并行训练 演示Demo详见视频
项目链接：https://github.com/maudzung/SFA3D
点击下面该视频，皆可查看演示Demo
Super Fast and Accurate 3D Object Detection based on 3D LiDAR Point Clouds
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
The technical details are described here
Update 2020.09.06: Add ROS source code. The great work has been done by @AhmedARadwan. The implementation is here
Demonstration (on a single GTX 1080Ti)
2. Getting Started
2.1. Requirement
The instructions for setting up a virtual environment is here.
代码语言：javascript复制git clone https://github.com/maudzung/SFA3D.git SFA3D
cd SFA3D/
pip install .
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:
代码语言：javascript复制cd sfa/data_process/
python kitti_dataset.py
2.3.2. Inference
The pre-trained model was pushed to this repo.
代码语言：javascript复制python test.py --gpu_idx 0 --peak_thresh 0.2
2.3.3. Making demonstration
代码语言：javascript复制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
代码语言：javascript复制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
代码语言：javascript复制python train.py --dist-url 'tcp://IP_OF_NODE1:FREEPORT' --multiprocessing-distributed --world-size 2 --rank 0 --batch_size 64 --num_workers 8
python train.py --dist-url 'tcp://IP_OF_NODE2:FREEPORT' --multiprocessing-distributed --world-size 2 --rank 1 --batch_size 64 --num_workers 8
- Second machine
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: nguyenmaudung93.kstn@gmail.com).
Thank you!
Citation
代码语言：javascript复制@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 [3] Libra_R-CNN: PyTorch Implementation
The YOLO-based models with the same BEV maps input: [4] Complex-YOLO: v4, v3, v2
3D LiDAR Point pre-processing: [5] VoxelNet: PyTorch Implementation
Folder structure
代码语言：javascript复制${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
└── sfa/
├── 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
