Robust phase unwrapping by probabilistic consensus
| Autor: | Mara Pistellato, Luca Cosmo, Andrea Torsello, Andrea Gasparetto, Andrea Albarelli, Filippo Bergamasco |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Gaussian Phase (waves) 02 engineering and technology 01 natural sciences Structured-light 3D scanner law.invention 010309 optics symbols.namesake law 3D reconstruction Fringe projection Maximum likelihood estimation Phase unwrapping Structured light 0103 physical sciences Code (cryptography) Electrical and Electronic Engineering Settore INF/01 - Informatica Mechanical Engineering Probabilistic logic 021001 nanoscience & nanotechnology Atomic and Molecular Physics and Optics Electronic Optical and Magnetic Materials Projector symbols Point location 0210 nano-technology Likelihood function Settore ING-INF/05 - Sistemi di Elaborazione delle Informazioni Algorithm |
| Popis: | Structured light scanning works by projecting over the scene a supplement of controlled information: the captured signal is processed to provide a unique label (namely a code) for each observed point, and then proceed to geometrical triangulation. In phase shift profilometry sinusoidal patterns are projected and each point is labelled according to the observed phase. Then, due to the periodic nature of the signal, a disambiguation method (known as phase unwrapping) is needed. Several unwrapping techniques have been proposed in the literature, since noisy signals lead to inaccuracies in phase estimation. This paper presents a novel phase unwrapping approach based on a probabilistic framework. The method involves the projection of multiple sinusoidal patterns with distinct period lengths, encoding different phase values at each point location. Phase values are then modelled as samples from a Wrapped Gaussian distribution with an unknown mean, determined by the projector code that generated the values. This formulation allows us to robustly perform phase unwrapping via Maximum Likelihood Estimation, recovering code values from the observed phases. Furthermore, the same likelihood function can be exploited to identify and correct faulty unwrappings by gauging mutual support in a spatial neighbourhood. An extensive experimental assessment validates the Gaussian distribution hypothesis and verifies the improvements in coding accuracy when compared to other classical unwrapping techniques. |
| Databáze: | OpenAIRE |
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