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Recovering Specular Surfaces Using Curved Line Images

Yuanyuan Ding 1 Jingyi Yu 1 Peter Sturm 2
2 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology
Abstract : We present a new shape-from-distortion framework for recovering specular (reflective/refractive) surfaces. While most existing approaches rely on accurate correspondences between 2D pixels and 3D points, we focus on analyzing the curved images of 3D lines which we call curved line images or CLIs. Our approach models CLIs of local reflections or refractions using the recently proposed general linear cameras (GLCs). We first characterize all possible CLIs in a GLC. We show that a 3D line will appear as a conic in any GLC. For a fixed GLC, the conic type is invariant to the position and orientation of the line and is determined by the GLC parameters. Furthermore, CLIs under single reflection/refraction can only be lines or hyperbolas. Based on our new theory, we develop efficient algorithms to use multiple CLIs to recover the GLC camera parameters. We then apply the curvature-GLC theory to derive the Gaussian and mean curvatures from the GLC intrinsics. This leads to a complete distortion-based reconstruction framework. Unlike conventional correspondence-based approaches that are sensitive to image distortions, our approach benefits from the CLI distortions. Finally, we demonstrate applying our framework for recovering curvature fields on both synthetic and real specular surfaces.
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Contributor : Peter Sturm Connect in order to contact the contributor
Submitted on : Thursday, May 14, 2009 - 4:37:50 PM
Last modification on : Tuesday, October 19, 2021 - 11:13:07 PM

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Yuanyuan Ding, Jingyi Yu, Peter Sturm. Recovering Specular Surfaces Using Curved Line Images. CVPR 2009 - IEEE International Conference on Computer Vision and Pattern Recognition, Jun 2009, Miami, United States. pp.2326-2333, ⟨10.1109/CVPR.2009.5206624⟩. ⟨inria-00384250⟩



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