The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-197

“Shape Reconstruction From Photometric Stereo”

by Kyoung Mu Lee and C.-C. Jay Kuo

February 1992

Two new iterative algorithms for shape reconstruction based on multiple images taken under different lighting conditions known as photometric stereo are proposed. In our previous work, an iterative SFS (Shape From Shading) algorithm using a single image was developed by combining a triangular element surface model with a linearized reflectance map. It is shown in this research that all single-image SFS algorithms share an inherent problem, i.e. the accuracy of the reconstructed surface height is related to the slope of the reflectance map function defined on the gradient space. This observation motivates us to generalize the single-image SFS algorithm to two photometric stereo SFS algorithms aiming at more accurate surface reconstruction. The two algorithms directly determine the surface height by minimizing a quadratic cost functional, which is defined to be the squares of the brightness error obtained from each individual image in a parallel or cascade manner. The optimal illumination condition which leads to best shape reconstruction is also derived. Simulation results for several test images are given to demonstrate the performance of our new algorithms.

To download the report in PDF format click here: USC-SIPI-197.pdf (2.6Mb)