HfPR

This code implements the photometric stereo method described in the paper "Height from Photometric Ratio with Model-based Light Source Selection". There are two parts to the method and they can be used independently:

1. The first part uses a probabilistic model to choose a subset of observations for each pixel with the goal of excluding shadows, specularities or other noise. This part requires guide surface normals and albedo which can be provided by any standard photometric stereo algorithm. We include implementations of a number of such methods. The best results are provided by using our RANSAC-based variant of classical photometric stereo.
2. The second part directly computes surface height from the images and the observation selections made in step 1. If you want to use all observations (for example if your object is approximately Lambertian and there aren't many cast shadows) then you can simply use all observations. The easiest way to do this is create the list of pairs as a cycle through all observations:

    for row=1:rows
        for col=1:cols
            for k=1:size(L,2)-1
                pairs{row,col}(k,:)=[k k+1];
            end
            pairs{row,col}(end+1,:)=[1 size(L,2)];
        end
    end

At both stages, you have the option of providing a binary foreground mask. If you don't wish to use a mask, simply pass all ones the same dimensions as the images.

If you only want to run the second part of the code, please invoke HfPR.m. If you only want to run the first part of the code, please invoke guided_PS.m, and comment out the last line

Demo Code

For a demonstration of this code repository, please run demo.m.

Reference

If you use this code in your research, please cite the following paper:

W. A. P. Smith and F. Fang. "Height from Photometric Ratio with Model-based Light Source Selection." Computer Vision and Image Understanding, Volume 145, pp. 128-138 (2016).

Bibtex:

@article{smith2016height,
    title={Height from Photometric Ratio with Model-based Light Source Selection},
    author={W. A. P. Smith and F. Fang},
    journal={Computer Vision and Image Understanding},
    volume={145},
    pages={128--138},
    year={2016},
    publisher={Elsevier}
}

Third Party Licenses

This repository ships with a copy of ransac.m from http://www.peterkovesi.com/matlabfns/Robust/ransac.m

Copyright (c) 2003-2013 Peter Kovesi
Centre for Exploration Targeting
The University of Western Australia
peter.kovesi at uwa edu au
http://www.csse.uwa.edu.au/~pk

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

The Software is provided "as is", without warranty of any kind.