A Bayesian Framework for Enhanced Geometric Reconstruction of Complex Objects by Helmholtz Stereopsis
Nadejda S. Roubtsova, Jean‐Yves Guillemaut · 2014
Abstract: Helmholtz stereopsis is an advanced 3D reconstruction technique for objects with arbitrary reflectance proper-ties that uniquely characterises surface points by both depth and normal. Traditionally, in Helmholtz stereopsis consistency of depth and normal estimates is assumed rather than explicitly enforced. Furthermore, conven-tional Helmholtz stereopsis performs maximum likelihood depth estimation without neighbourhood consid-eration. In this paper, we demonstrate that reconstruction accuracy of Helmholtz stereopsis can be greatly enhanced by formulating depth estimation as a Bayesian maximum a posteriori probability problem. In re-formulating the problem we introduce neighbourhood support by formulating and comparing three priors: a depth-based, a normal-based and a novel depth-normal consistency enforcing one. Relative performance eval-uation of the three priors against standard maximum likelihood Helmholtz stereopsis is performed on both real and synthetic data to facilitate both qualitative and quantitative assessment of reconstruction accuracy. Observed superior performance of our depth-normal consistency prior indicates a previously unexplored ad-vantage in joint optimisation of depth and normal estimates. 1