(ADfSM: All-around Depth from Small Motion)
Source code and datasets for the paper:
S. Im, H. Ha, F. Rameau, H.-G. Jeon, G. Choe and I.S. Kweon, All-around Depth from Small Motion with A Spherical Panoramic Camera - ECCV 2016
- run main.m
Important Information
Frame selection
The current implementation uses only the first 30 frames of your video clip. If you want to try with a different number of images or different sampling rate, please change 'setting.sam_rate'.
Dense matching step
We implemented the function "DenseMatching" to receive a scale for image downsampling and the number of labels for your convenience in testing. (Default: 0.5 scale and 64 labels for quick tests, you can change them ('setting.scaling, 'setting.num_label')
For the depth refinement, we utilized a tree-based depth upsampling approach [1,2].
© 2017 Sunghoon Im, Korea Advanced Institute of Science and Technology (KAIST)
IMPORTANT: If you use this software please cite the following in any resulting publication:
@inproceedings{im2016all,
title={All-Around Depth from Small Motion with a Spherical Panoramic Camera},
author={Im, Sunghoon and Ha, Hyowon and Rameau, Fran{\c{c}}ois and Jeon, Hae-Gon and Choe, Gyeongmin and Kweon, In So},
booktitle={European Conference on Computer Vision},
pages={156--172},
year={2016},
organization={Springer}
}
- Yang, Qingxiong. "Stereo matching using tree filtering." IEEE transactions on pattern analysis and machine intelligence 37.4 (2015): 834-846.
- Yang, Qingxiong. "A non-local cost aggregation method for stereo matching." Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 2012.