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      Full 3D Reconstruction of Transparent Objects

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          Abstract

          Numerous techniques have been proposed for reconstructing 3D models for opaque objects in past decades. However, none of them can be directly applied to transparent objects. This paper presents a fully automatic approach for reconstructing complete 3D shapes of transparent objects. Through positioning an object on a turntable, its silhouettes and light refraction paths under different viewing directions are captured. Then, starting from an initial rough model generated from space carving, our algorithm progressively optimizes the model under three constraints: surface and refraction normal consistency, surface projection and silhouette consistency, and surface smoothness. Experimental results on both synthetic and real objects demonstrate that our method can successfully recover the complex shapes of transparent objects and faithfully reproduce their light refraction properties.

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          Most cited references17

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          Screened poisson surface reconstruction

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            Efficient and flexible sampling with blue noise properties of triangular meshes.

            This paper deals with the problem of taking random samples over the surface of a 3D mesh describing and evaluating efficient algorithms for generating different distributions. We discuss first the problem of generating a Monte Carlo distribution in an efficient and practical way avoiding common pitfalls. Then, we propose Constrained Poisson-disk sampling, a new Poisson-disk sampling scheme for polygonal meshes which can be easily tweaked in order to generate customized set of points such as importance sampling or distributions with generic geometric constraints. In particular, two algorithms based on this approach are presented. An in-depth analysis of the frequency characterization and performance of the proposed algorithms are also presented and discussed.
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              Edge-aware point set resampling

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                Author and article information

                Journal
                09 May 2018
                Article
                1805.03482
                b8ab3bb7-4ac4-4a3a-bef6-95dad7b1bc9c

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                Conditionally accepted to SIGGRAPH 2018
                cs.GR cs.CV

                Computer vision & Pattern recognition,Graphics & Multimedia design
                Computer vision & Pattern recognition, Graphics & Multimedia design

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