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A Theory of Refractive Photo-Light-Path Triangulation

Visesh Chari 1 Peter Sturm 2
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
2 STEEP [2010-2015] - Sustainability transition, environment, economy and local policy [2010-2015]
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
Abstract : 3D reconstruction of transparent refractive objects like a plastic bottle is challenging: they lack appearance related visual cues and merely reflect and refract light from the surrounding environment. Amongst several approaches to reconstruct such objects, the seminal work of Light-Path triangulation is highly popular because of its general applicability and analysis of minimal scenarios. A light-path is defined as the piece-wise linear path taken by a ray of light as it passes from source, through the object and into the camera. Transparent refractive objects not only affect the geometric configuration of light-paths but also their radiometric properties. In this paper, we describe a method that combines both geometric and radiometric information to do reconstruction. We show two major consequences of the addition of radiometric cues to the light-path setup. Firstly, we extend the case of scenarios in which reconstruction is plausible while reducing the minimal requirements for a unique reconstruction. This happens as a consequence of the fact that radiometric cues add an additional known variable to the already existing system of equations. Secondly, we present a simple algorithm for reconstruction, owing to the nature of the radiometric cue. We present several synthetic experiments to validate our theories, and show high quality reconstructions in challenging scenarios.
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Submitted on : Wednesday, November 6, 2013 - 11:10:31 AM
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Visesh Chari, Peter Sturm. A Theory of Refractive Photo-Light-Path Triangulation. CVPR 2013 - IEEE Conference on Computer Vision and Pattern Recognition, Jun 2013, Portland, United States. pp.1438-1445, ⟨10.1109/CVPR.2013.189⟩. ⟨hal-00880470⟩



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