Authors:
Nadejda Roubtsova
and
Jean-Yves Guillemaut
Affiliation:
University of Surrey, United Kingdom
Keyword(s):
3D Reconstruction, Helmholtz Stereopsis, Complex Reflectance.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image-Based Modeling
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Software Engineering
;
Stereo Vision and Structure from Motion
Abstract:
Helmholtz stereopsis is an advanced 3D reconstruction technique for objects with arbitrary reflectance properties
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, conventional
Helmholtz stereopsis performs maximum likelihood depth estimation without neighbourhood consideration.
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 reformulating
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 evaluation
of the three priors against standard maximum likelihood Helmholtz stereopsis is performed on both
real and synthetic data to facilitate both qual
itative and quantitative assessment of reconstruction accuracy.
Observed superior performance of our depth-normal consistency prior indicates a previously unexplored advantage
in joint optimisation of depth and normal estimates.
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