TCMonoDepth

<h2 align="center">TCMonoDepth: Enforcing Temporal Consistency in Video Depth Estimation</h2> <img src="figs/tennis.gif" width="100%">

<p align="center">TCMonoDepth is a method for stable depth estimation for any video.</p>

<p align="center">TCMonoDepth 是一个为任意视频估计稳定的深度值的模型。</p>

<p align="center"> <a href="https://openaccess.thecvf.com/content/ICCV2021W/PBDL/papers/Li_Enforcing_Temporal_Consistency_in_Video_Depth_Estimation_ICCVW_2021_paper.pdf">Paper</a> </p>

Usage

Requirements

• [x] python
• [x] pytorch
• [x] torchvision
• [x] opencv
• [x] tqdm

Testing

You can download our pretraind checkppont from link (google drive) or link (百度云, 提取码: w2kr) and save it in the./weights folder. Put your video into the folder videos and run

cd TCMonoDepth
python demo.py --model large --resume ./weights/_ckpt.pt.tar --input ./videos --output ./output --resize_size 384

A small MonoDepth model for mobile devices

A lightweight and very fast monodepth model

cd TCMonoDepth
python demo.py --model small --resume ./weights/_ckpt_small.pt.tar --input ./videos --output ./output --resize_size 256

Bibtex

If you use this code for your research, please consider to star this repo and cite our paper.

@inproceedings{li2021enforcing,
 title={Enforcing Temporal Consistency in Video Depth Estimation},
 author={Li, Siyuan and Luo, Yue and Zhu, Ye and Zhao, Xun and Li, Yu and Shan, Ying},
 booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
 year={2021}
}

Acknowledgement

In this project, parts of the code are adapted from: MiDaS. We thank the authors for sharing codes for their great works.