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Fast Hybrid Approach for Texturing Point Models
Author(s) -
Zhang Haitao,
Qiu Feng,
Kaufman Arie
Publication year - 2004
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2004.00804.x
Subject(s) - classification of discontinuities , discontinuity (linguistics) , point (geometry) , parameterized complexity , computer science , algorithm , matching (statistics) , distortion (music) , sample (material) , texture synthesis , bounded function , point cloud , texture (cosmology) , artificial intelligence , computer vision , mathematics , geometry , image (mathematics) , image texture , image processing , mathematical analysis , amplifier , computer network , statistics , chemistry , bandwidth (computing) , chromatography
We present three methods for texturing point models from sample textures. The first method, the point parameterization method, uses a fast distortion‐bounded parameterization algorithm to flatten the point model's surface into one or more 2D patches. The sample texture is mapped onto these patches and alpha blending is used to minimize the discontinuity in the gaps between the patches. The second method is based on neighborhood matching where a color is assigned to each point by searching the best match within an irregular neighborhood. The hybrid method combines the former two methods, capitalizing on the advantages of both. The point parameterization method is used first to color most of the points, and the point neighborhood‐matching method is then applied to the points belonging to the gaps between the parameterized patches to minimize the discontinuity. We opt for fast texture generation, while some discontinuities may appear in the gaps of anisotropic textures.