This directory contains a reference implementation of the algorithm described in the paper:
Ayan Chakrabarti and Kalyan Sunkavalli, "Single-image RGB Photometric Stereo With Spatially-varying Albedo", Proc. of the IEEE Intl. Conf. on 3D Vision (3DV) 2016.
It is being released for non-commercial research use only. If you find this code useful in your research, we request that you cite the above paper. Along with the implementation source code, we are also releasing real datasets captured under the RGB-PS setup in the data/ sub-directory of this repository/distribution. These datasets are owned by Adobe Systems, and are being released under a CC-by-attribution license: please see the data/LICSENSE.txt and data/README.md files for details.
Our implementation requires a modern version of MATLAB with support for the gpuArray class, a CUDA-capable GPU (our experiments were run on a Titan X), as well as a distribution of CUDA (with the nvcc compiler).
You will first need to compile the mex function in getSSD.cu. Please see the documentation for your version of MATLAB to figure out how to compile mex files with CUDA code. If you have the latest version of MATLAB and all paths set up, this should be as simple as:
>> mexcuda getSSD.cuThe main function to do estimation is doRGBPS. Given an input RGB image, a mask, and the lighting matrix of the RGB-PS setup, this function returns estimated normals for the surface. Please use help doRGBPS in MATLAB to get detailed instructions on usage. We also provide a simple utility function getZ that can be used to integrate these normals to get a depth map.
Please see the project page and contact ayanc@ttic.edu with any questions.