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Constrained Texture Mapping via Approximate Voronoi Base Domain

Published: 11 June 2018 Publication History

Abstract

In this paper, we propose an approximate Voronoi base domain method to address constrained texture mapping problem. In particular, we generate approximate Voronoi base domains on the input texture image plane and planar embedding of mesh surface. Then, we propose to leverage the triangulations of constraint points on both sides, and yield the same topology for the two approximate Voronoi base domains. Based on the equivalent topology, we are able to employ generalized barycentric coordinates between pairwise Voronoi cells of the two approximate Voronoi base domains. The proposed method is highly efficient, and it allows to edit constraints in real time. Experimental results show the improved effectiveness and robustness of the proposed method for constraint texture mapping.

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    cover image ACM Other conferences
    CGI 2018: Proceedings of Computer Graphics International 2018
    June 2018
    284 pages
    ISBN:9781450364010
    DOI:10.1145/3208159
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 11 June 2018

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    Author Tags

    1. Constrained texture mapping
    2. Delaunay triangulation
    3. Surface parameterization
    4. Voronoi diagram
    5. Wachspress coordinates

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    CGI 2018
    CGI 2018: Computer Graphics International 2018
    June 11 - 14, 2018
    Island, Bintan, Indonesia

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    CGI 2018 Paper Acceptance Rate 35 of 159 submissions, 22%;
    Overall Acceptance Rate 35 of 159 submissions, 22%

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