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Structure-Guided Ranking Loss for Single Image Depth Prediction

This repository contains a pytorch implementation of our CVPR2020 paper "Structure-Guided Ranking Loss for Single Image Depth Prediction". Project Page

Changelog

• [Jun. 2020] Initial release

To do

• Mix data training

Prerequisites

• Pytorch >= 0.4.1
• CUDA >= 0.8
• Python >= 2.7
• glob, matplotlib
• Need to compile the syncbn module in models/syncbn. Note that the directory of the syncbn module should be modified in some .py files (i.e., DepthNet.py, resnet.py and networks.py)
• Download the model.pth.tar

Inference

# Before running, you should set the CUDA_VISIBLE_DEVICES in demo.sh
bash demo.sh

If you find our work useful in your research, please consider citing the paper.

@InProceedings{Xian_2020_CVPR,
author = {Xian, Ke and Zhang, Jianming and Wang, Oliver and Mai, Long and Lin, Zhe and Cao, Zhiguo},
title = {Structure-Guided Ranking Loss for Single Image Depth Prediction},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

Dataset

Our HRWSI dataset is for research only! Some researchers may interested in the stereo data, so we provide the right views here. Please let me know if you have any questions.

Lisence

Research only