Official implementation of paper Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields(CVPR 2020) paper link

NYUv2OC++ dataset(only for test use) download link

• PyTorch >= 0.4
• OpenCV
• CUDA >= 8.0(Only tested with CUDA >= 8.0)
• Easydict

sh download.sh

#Use depth only as input
cd model/nyu/df_nyu_depth_only
python train.py -d 0
#Use RGB image as guidance
cd model/nyu/df_nyu_rgb_guidance
python train.py -d 0

@InProceedings{Ramamonjisoa_2020_CVPR,
author = {Ramamonjisoa, Michael and Du, Yuming and Lepetit, Vincent},
title = {Predicting Sharp and Accurate Occlusion Boundaries in Monocular Depth Estimation Using Displacement Fields},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

The model can be trained with only synthetic data(Scenenet for example), and generalize naturally on real data.

The code is based on TorchSeg

The NYUv2-OC++ is annotated manually by 4 PhD students major in computer vision. Special thanks to Yang Xiao and Xuchong Qiu for their help in annotating the NYUv2-OC++ dataset.