Pose Estimation and 3D Model Alignment using Error Function Minimization in Silhouette Images

Since, most of the descriptors of 3D models are not invariant to various transformations and differentiations, the alignment of 3D models is one of the most important steps to achieve high precision 3D model retrieval system. In this paper, a method is presented to estimate the different pose of triangular mesh model in 3D space using Nelder-Mead optimization algorithm with non-overlapping pixels of each pair of 2D silhouettes for many viewing angles as cost function. So, after applying the translation and scale standardization for 3D models, in each class of considered database, a favorite model is selected as the example and the other models are rotated in such a way to reach the most similar 3D pose of example model. The overall performance of the suggested framework is evaluated using McGill 3D models Database. The numerical results obtained from different experiments prove the ability of proposed algorithm in 3D model alignment. For example, in airplane 3D model with silhouettes size, the error of alignment is 36437 pixels. This error equals to 6.8% of total area of whole 2D silhouette views of fixed moving 3D models.