A neural network approach to photometric stereo inversion of real-world reflectance maps for extracting 3-D shapes of objects

Presents a neural network approach to the problem of photometric stereo inversion of the reflectance maps of real-world objects for the purpose of estimating the 3-D attitudes of the surface patches of objects. As in the photometric stereo approach, here also the observation that there is a one-to-one mapping between the n-tuples of the photometric stereo image intensities and the orientations of the surface normals is valid. A multilayered feedforward neural network with backpropagation training algorithm is used as dimensionality reducer to effectively encode this mapping by associating the two components of surface normals to the observed intensities from three photometric stereo images of the underlying surface patches. The training patterns are sampled from the images of a Gaussian sphere of average reflectance containing both diffuse and specular components. The neural network thus trained has been tested on images of real-world objects with different shapes and reflectance properties. Using the surface normals estimated by the neural network, 3-D shapes of the objects have been reconstructed to a good approximation