https://github.com/lzccccc/SMOKE
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
https://github.com/lzccccc/SMOKE
3d-object-detection autonomous-driving
Last synced: about 1 year ago
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SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
- Host: GitHub
- URL: https://github.com/lzccccc/SMOKE
- Owner: lzccccc
- License: mit
- Created: 2020-01-13T09:40:19.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-09-16T15:25:21.000Z (over 4 years ago)
- Last Synced: 2024-10-28T07:39:40.572Z (over 1 year ago)
- Topics: 3d-object-detection, autonomous-driving
- Language: Python
- Homepage:
- Size: 9.66 MB
- Stars: 704
- Watchers: 21
- Forks: 177
- Open Issues: 60
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
[Video](https://www.youtube.com/watch?v=pvM_bASOQmo)
This repository is the official implementation of our paper [SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation](https://arxiv.org/pdf/2002.10111.pdf).
For more details, please see our paper.
## Introduction
SMOKE is a **real-time** monocular 3D object detector for autonomous driving.
The runtime on a single NVIDIA TITAN XP GPU is **~30ms**.
Part of the code comes from [CenterNet](https://github.com/xingyizhou/CenterNet),
[maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark),
and [Detectron2](https://github.com/facebookresearch/detectron2).
The performance on KITTI 3D detection (3D/BEV) is as follows:
| | Easy | Moderate | Hard |
|-------------|:-------------:|:--------------:|:------------:|
| Car | 14.17 / 21.08 | 9.88 / 15.13 | 8.63 / 12.91 |
| Pedestrian | 5.16 / 6.22 | 3.24 / 4.05 | 2.53 / 3.38 |
| Cyclist | 1.11 / 1.62 | 0.60 / 0.98 | 0.47 / 0.74 |
The pretrained weights can be downloaded [here](https://drive.google.com/open?id=11VK8_HfR7t0wm-6dCNP5KS3Vh-Qm686-).
## Requirements
All codes are tested under the following environment:
* Ubuntu 16.04
* Python 3.7
* Pytorch 1.3.1
* CUDA 10.0
## Dataset
We train and test our model on official [KITTI 3D Object Dataset](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
Please first download the dataset and organize it as following structure:
```
kitti
│──training
│ ├──calib
│ ├──label_2
│ ├──image_2
│ └──ImageSets
└──testing
├──calib
├──image_2
└──ImageSets
```
## Setup
1. We use `conda` to manage the environment:
```
conda create -n SMOKE python=3.7
```
2. Clone this repo:
```
git clone https://github.com/lzccccc/SMOKE
```
3. Build codes:
```
python setup.py build develop
```
4. Link to dataset directory:
```
mkdir datasets
ln -s /path_to_kitti_dataset datasets/kitti
```
## Getting started
First check the config file under `configs/`.
We train the model on 4 GPUs with 32 batch size:
```
python tools/plain_train_net.py --num-gpus 4 --config-file "configs/smoke_gn_vector.yaml"
```
For single GPU training, simply run:
```
python tools/plain_train_net.py --config-file "configs/smoke_gn_vector.yaml"
```
We currently only support single GPU testing:
```
python tools/plain_train_net.py --eval-only --config-file "configs/smoke_gn_vector.yaml"
```
## Acknowledgement
[CenterNet](https://github.com/xingyizhou/CenterNet)
[maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark)
[Detectron2](https://github.com/facebookresearch/detectron2)
## Citations
Please cite our paper if you find SMOKE is helpful for your research.
```
@article{liu2020SMOKE,
title={{SMOKE}: Single-Stage Monocular 3D Object Detection via Keypoint Estimation},
author={Zechen Liu and Zizhang Wu and Roland T\'oth},
journal={arXiv preprint arXiv:2002.10111},
year={2020}
}
```