[CVPR 2019] Monocular Depth Estimation Using Relative Depth Maps

Paper

Monocular Depth Estimation Using Relative Depth Maps

(Supplemental) Monocular Depth Estimation Using Relative Depth Maps

If you use our code or results, please cite:

@InProceedings{Lee_2019_CVPR,
  author = {Lee, Jae-Han and Kim, Chang-Su},
  title = {Monocular Depth Estimation Using Relative Depth Maps}, 
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2019}
}

Snapshot

You can download our trained caffemodel from the following link: default_mode_net.caffemodel

Dataset

You should download 'nyu_depth_v2_labeled.mat' and 'splits.mat' files from official NYUv2 site: nyu_depth_v2_labeled.mat, splits.mat

Results

The results of our algorithm of 654 test images of NYUv2 set are located in 'results/depth_map'. All depth maps are stored as png files, and each pixel consists of 16 bits of data. You can convert png files to depth values in the following ways:

png_depth = imread('depth_test001.png');
depth = double(png_depth) / (2^16-1) * 10;