Improved photometric stereo based on local search

Photometric stereo methods seek to reconstruct 3D objects using multiple images captured under varied illumination directions. Nevertheless, shadows are still among the most significant problems faced by the photometric stereo and most of the existing formulations disregard this problem although the elimination of shadow greatly improves the results. Usually, authors define empirically a threshold to eliminate pixels that have low brightness. Accordingly, in this paper we present an improved approach to enhance the photometric stereo. Our aim consists to propose an improved formulation for solving the shadow problem and determine the optimal solution. In order to define the threshold value used to solve the shadow problem, we propose an improvement of an existing formulation. Our formulation normalizes the error rate with respect to the threshold, which makes it possible to compare the error rates of a different threshold values. A second contribution consists to find the optimal solution of the normal vectors by adapting the local search method “Tabu search meta-heuristic” to find the optimal solution in the neighborhood of the initial solution. We perform several tests on real objects of different complexity with different parameters values. In order to show the effectiveness of our proposal, a number of comparisons with recent published methods are made. Through these experiments, we show that our proposed method outperforms modern near-field photometric stereo approaches in terms of quality and application that does not require manual intervention.