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A Framework for Automatically Recovering Object Shape, Reflectance and Light Sources from Calibrated Images

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Abstract

In this paper, we present a complete framework for recovering an object shape, estimating its reflectance properties and light sources from a set of images. The whole process is performed automatically. We use the shape from silhouette approach proposed by R. Szeliski (1993) combined with image pixels for reconstructing a triangular mesh according to the marching cubes algorithm. A classification process identifies regions of the object having the same appearance. For each region, a single point or directional light source is detected. Therefore, we use specular lobes, lambertian regions of the surface or specular highlights seen on images. An identification method jointly (i) decides what light sources are actually significant and (ii) estimates diffuse and specular coefficients for a surface represented by the modified Phong model (Lewis, 1994). In order to validate our algorithm efficiency, we present a case study with various objects, light sources and surface properties. As shown in the results, our system proves accurate even for real objects images obtained with an inexpensive acquisition system.

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Authors and Affiliations

  1. SIC Laboratory, University of Poitiers, Bât. SP2MI, Bd M. et P. Curie, 86962, Futuroscope Chasseneuil, France

    Bruno Mercier & Daniel Meneveaux

  2. Department of Computer Science, Imager Lab, University of British Columbia, X635-2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada

    Alain Fournier

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  1. Bruno Mercier
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  2. Daniel Meneveaux
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  3. Alain Fournier
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Correspondence to Bruno Mercier.

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Mercier, B., Meneveaux, D. & Fournier, A. A Framework for Automatically Recovering Object Shape, Reflectance and Light Sources from Calibrated Images. Int J Comput Vision 73, 77–93 (2007). https://doi.org/10.1007/s11263-006-9273-y

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  • DOI: https://doi.org/10.1007/s11263-006-9273-y

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