https://github.com/wxinlong/foresee
Task-Aware Monocular Depth Estimation for 3D Object Detection, AAAI2020
https://github.com/wxinlong/foresee
monocular-depth-estimation
Last synced: 11 months ago
JSON representation
Task-Aware Monocular Depth Estimation for 3D Object Detection, AAAI2020
- Host: GitHub
- URL: https://github.com/wxinlong/foresee
- Owner: WXinlong
- Created: 2019-12-09T10:06:32.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2021-01-11T00:36:15.000Z (over 5 years ago)
- Last Synced: 2025-04-12T20:16:03.326Z (about 1 year ago)
- Topics: monocular-depth-estimation
- Language: Python
- Homepage:
- Size: 304 KB
- Stars: 61
- Watchers: 9
- Forks: 8
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Task-Aware Monocular Depth Estimation for 3D Object Detection
This project hosts the code for implementing the ForeSeE algorithm for depth estimation.
> [**Task-Aware Monocular Depth Estimation for 3D Object Detection**](https://arxiv.org/abs/1909.07701),
> Xinlong Wang, Wei Yin, Tao Kong, Yuning Jiang, Lei Li, Chunhua Shen
> *AAAI, 2020*
## Installation
This implementation is based on [VNL](https://github.com/YvanYin/VNL_Monocular_Depth_Prediction). Please refer to [INSTALL.md](INSTALL.md) for installation.
## Dataset
Please refer to [KITTI dataset](http://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction) for details.
The annotation files of [KITTI Object subset](https://github.com/WXinlong/ForeSeE/tree/master/datasets/KITTI_object/annotations) used in our work are provided.
## Models
Download the trained model from this [link](https://cloudstor.aarnet.edu.au/plus/s/M3LFxiDPZkMKrtw) and put it under experiments/foresee/.
## Testing
cd experiments/foresee
sh test.sh
## Training
cd experiments/foresee
sh train.sh
## Citations
Please consider citing our papers in your publications if the project helps your research. BibTeX reference is as follows.
```
@InProceedings{wang2020foresee,
title={Task-Aware Monocular Depth Estimation for 3D Object Detection},
author = {Wang, Xinlong and Yin, Wei and Kong, Tao and Jiang, Yuning, and Li, Lei and Shen, Chunhua},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},
year={2020}
}
```