Created by Lu Rui.

Occlusion relationship reasoning of objects from monocular images are fundamental in computer vision and mobile robot applications. Furthermore, it can be used for scene understanding and perception, such as object detection, segmentation and 3D reconstruction. From the perspective of the observer, occlusion relationship reflects relative depth difference between objects in the scene.

The Data Preparation and Evaluation are following Guoxia Wang with his DOOBNet(https://github.com/GuoxiaWang/DOOBNet). Thanks for his valuable work.

If you find OFNet useful in your research, please consider citing:

@inproceedings{Lu2019Occlusion,
  Title = {Occlusion-shared and Feature-separated Network for Occlusion Relationship Reasoning},
  Author = {Rui Lu, Feng Xue, Menghan Zhou, Anlong Ming, Yu Zhou},
  Booktitle = {ICCV},
  Year = {2019}
}

You may download the dataset original images from PASCAL VOC 2010 and annotations from here. Then you should copy or move JPEGImages folder in PASCAL VOC 2010 and Data folder and val_doc_2010.txt in PIOD to data/PIOD/. You will have the following directory structure:

PIOD
|_ Data
|  |_ <id-1>.mat
|  |_ ...
|  |_ <id-n>.mat
|_ JPEGImages 
|  |_ <id-1>.jpg
|  |_ ...
|  |_ <id-n>.jpg
|_ val_doc_2010.txt

Now, you can use data convert tool to augment and generate HDF5 format data for OFNet.

mkdir data/PIOD/Augmentation

python doobscripts/doobnet_mat2hdf5_edge_ori.py \
--dataset PIOD \
--label-dir data/PIOD/Data \
--img-dir data/PIOD/JPEGImages \
--piod-val-list-file data/PIOD/val_doc_2010.txt \
--output-dir data/PIOD/Augmentation

For BSDS ownership dataset, you may download the dataset original images from BSDS300 and annotations from here. Then you should copy or move BSDS300 folder in BSDS300-images and trainfg and testfg folder in BSDS_theta to data/BSDSownership/. And you will have the following directory structure:

BSDSownership
|_ trainfg
|  |_ <id-1>.mat
|  |_ ...
|  |_ <id-n>.mat
|_ testfg
|  |_ <id-1>.mat
|  |_ ...
|  |_ <id-n>.mat
|_ BSDS300
|  |_ images
|     |_ train
|        |_ <id-1>.jpg
|        |_ ...
|        |_ <id-n>.jpg
|     |_ ...
|  |_ ...

Note that BSDS ownership's test set are split from 200 train images (100 for train, 100 for test). More information you can check ids in trainfg and testfg folder and ids in BSDS300/images/train folder, or refer to here

Run the following code for BSDS ownership dataset.

mkdir data/BSDSownership/Augmentation

python doobscripts/doobnet_mat2hdf5_edge_ori.py \
--dataset BSDSownership \
--label-dir data/BSDSownership/trainfg \
--img-dir data/BSDSownership/BSDS300/images/train \
--bsdsownership-testfg data/BSDSownership/testfg \
--output-dir data/BSDSownership/Augmentation 

Firstly, you need to download the Res50 weight file from Res50 and save resnet50.caffemodel to the folder $OFNET_ROOT/models/resnet/.

For training OFNet on PIOD training dataset, you can run:

cd $OFNET_ROOT/examples/ofnet/PIOD

./train.sh

When training completed, you need to modify model = '../models/ofnet_piod.caffemodel' in deploy_ofnet_piod.py and then run python deploy_ofnet_piod.py to get the results on PIOD testing dataset. For comparation, you can also download our trained model from here. (code: y4b6). The testing results are available at here. (code: 85ih).

For training OFNet on BSDS ownership, you can refer the manner as same as PIOD dataset above. The testing results are available at here. (code: xj8q).

Here we provide the PIOD and the BSDS ownership dataset's evaluation and visualization code in doobscripts folder.

Note that you need to config the necessary paths or variables. More information please refers to doobscripts/README.md.

To run the evaluation:

run doobscripts/evaluation/EvaluateOcc.m

For visualization, to run the script:

run doobscripts/visulation/PlotAll.m

We would like to thank Guoxia Wang for helping with generating DOOBNet experimental results and valuable discussions.