DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.

This repository contains the source code of our paper: Wei Yin, Xinlong Wang, Chunhua Shen, Yifan Liu, Zhi Tian, Songcen Xu, Changming Sun, DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data.

Some Results

Some Dataset Examples

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Hightlights

• Generalization: We have tested on several zero-shot datasets to test the generalization of our method.

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Installation

• Please refer to Installation.

Datasets

We collect multiply source data to construct our DiverseDepth dataset, including crawling online stereoscopic images, images from DIML and Taskonomy. These three parts form the foreground parts (Part-fore), outdoor scenes (Part-out) and indoor scenes (Part-in) of our dataset. The size of three parts are: Part-in: contains 93838 images Part-out: contains 120293 images Part-fore: contains 109703 images We will release the dataset as soon as possible.

Model Zoo

• ResNext50_32x4d backbone, trained on DiverseDepth dataset, download here

Inference

# Run the inferece on NYUDV2 dataset
 python  ./tools/test_diversedepth_nyu.py \
		--dataroot    ./datasets/NYUDV2 \
		--dataset     nyudv2 \
		--cfg_file     lib/configs/resnext50_32x4d_diversedepth_regression_vircam \
		--load_ckpt   ./model.pth 
		
# Test depth predictions on any images, please replace the data dir in test_any_images.py
 python  ./tools/test_any_diversedepth.py \
		--dataroot    ./ \
		--dataset     any \
		--cfg_file     lib/configs/resnext50_32x4d_diversedepth_regression_vircam \
		--load_ckpt   ./model.pth 

If you want to test the kitti dataset, please see here

Citation

@article{yin2020diversedepth,
  title={DiverseDepth: Affine-invariant Depth Prediction Using Diverse Data},
  author={Yin, Wei and Wang, Xinlong and Shen, Chunhua and Liu, Yifan and Tian, Zhi and Xu, Songcen and Sun, Changming and Renyin, Dou},
  journal={arXiv preprint arXiv:2002.00569},
  year={2020}
}

Contact

Wei Yin: wei.yin@adelaide.edu.au