A statistical error analysis for voxel coloring

This paper presents an error analysis for voxel coloring [S.M. Seitz, et al. (1997), K.N. Kutulakos, et al. (2000)], which is one of the well known methods to reconstruct 3D shape from 2D calibrated multiple-view images. In order to analyze the errors arising in the reconstruction process of voxel coloring algorithms, we first model several noise sources in the analytic or statistical way, and then examine the effects of each noise component on the reconstructed 3D model. Specifically, in order to analyze the statistical errors, we focus on the distribution of the image variance, which is employed as photo consistency measurement. And also, we show that how specular components induce errors in reconstructing 3D model. The results of this analysis are very useful for evaluating the statistical confidence of the reconstructed 3D model as well as finding the optimal threshold for the occupancy decision.