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Direct Differential Photometric Stereo Shape Recovery of Diffuse and Specular Surfaces

Accepted version
Peer-reviewed

Repository DOI


Type

Article

Change log

Authors

Tozza, S 
Mecca, R 
Duocastella, M 
Del Bue, A 

Abstract

Recovering the 3D shape of an object from shading is a challenging problem due to the complexity of modeling light propagation and surface reflections. Photometric Stereo (PS) is broadly considered a suitable approach for high-resolution shape recovery, but its functionality is restricted to a limited set of object surfaces and controlled lighting setup. In particular, PS models generally consider reflection from objects as purely diffuse, with specularities being regarded as a nuisance that breaks down shape reconstruction. This is a serious drawback for implementing PS approaches, since most common materials have prominent specular components. In this paper, we propose a PS model that solves the problem for both diffuse and specular components aimed at shape recovery of generic objects with the approach being independent of the albedo values thanks to the image ratio formulation used. Notably, we show that by including specularities, it is possible to solve the PS problem for a minimal number of three images using a setup with three calibrated lights and a standard industrial camera. Even if an initial separation of diffuse and specular components is still required for each input image, experimental results on synthetic and real objects demonstrate the feasibility of our approach for shape reconstruction of complex geometries.

Description

This is the author accepted manuscript. The final version is available from Springer via http://dx.doi.org/10.1007/s10851-016-0633-0

Keywords

photometric stereo, Blinn–Phong model, image ratio

Journal Title

Journal of Mathematical Imaging and Vision

Conference Name

Journal ISSN

0924-9907
1573-7683

Volume Title

56

Publisher

Springer Science and Business Media LLC
Sponsorship
The first author acknowledges the support of INDAM under the GNCS research Project “Metodi numerici per la regolarizzazione nella ricostruzione feature-preserving di dati.”