Texture mapping progressive meshes
By Pedro V. Sander, John M. Snyder, Steven J. Gortler, Hugues Hoppe. International Conference on Computer Graphics and Interactive Techniques, 2001
750 citations recorded by the source corpus.
Given an arbitrary mesh, we present a method to construct a progressive mesh (PM) such that all meshes in the PM sequence share a common texture parametrization. Our method considers two important goals simultaneously. It minimizes texture stretch (small texture distances mapped onto large surface distances) to balance sampling rates over all locations and directions on the surface. It also minimizes texture deviation (“slippage” error based on parametric correspondence) to obtain accurate textured mesh approximations. The method begins by partitioning the mesh into charts using planarity and compactness heuristics. It creates a stretch-minimizing parametrization within each chart, and resizes the charts based on the resulting stretch. Next, it simplifies the mesh while respecting the chart boundaries. The parametrization is re-optimized to reduce both stretch and deviation over the whole PM sequence. Finally, the charts are packed into a texture atlas. We demonstrate using such atlases to sample color and normal maps over several models.
Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design
View Paper: https://www.semanticscholar.org/paper/2fab262ddc9879a4db04c19a789b82992c126ed1
How researchers see this paper
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In Computational Mechanics
- Bruno Lévy
- Alla Sheffer
- Kai Hormann
- Craig Gotsman
- Xianfeng David Gu
- Ayellet Tal
- Sagi Katz
- Michela Spagnuolo
In Computer Graphics and Computer-Aided Design
- Bruno Lévy
- Alla Sheffer
- Kai Hormann
- Craig Gotsman
- Xianfeng David Gu
- Ayellet Tal
- Sagi Katz
- Michela Spagnuolo
Citations in the corpus
- Global conformal surface parameterization (2003, background): “Sander et al. 29 develop a stretch stretch and texture deviation. to minimize texture , Sander et al. 26 design the signal-stretch parameterization metric to measure signal error.”
- Cross-parameterization and compatible remeshing of 3D models (2004, methodology): “The L2 stretch is measured as described in [Sander et al. 2001].”
- Mesh segmentation using feature point and core extraction (2005, background): “Some of these applications require segmentation into higher level meaningful components while others require segmentation into lower level disk-like patches [5, 10, 26, 34].”
- Fundamentals of spherical parameterization for 3D meshes (2003, background): “Most of the recent works on the subject of parameterization (e.g. [Desbrun et al. 2002; Floater 1997; Levy et al. 2002; Sander et al. 2001; Sheffer and de Sturler 2001]) have focused on defining the distortion, and showing how to minimize it.”
- ABF++: fast and robust angle based flattening (2005, methodology): “Run-times for the stretch minimizing method [Sander et al. 2001] were provided by the authors.”
- Geometry images (2002, methodology): “As shown by the analysis in [17], the L(2) geometric-stretch metric introduced in [18] is in fact an approximation of this ideal measure.”
- Feature-based surface parameterization and texture mapping (2005, methodology): “The stretch metric of Sander (used in [Sander et al. 2001; Lévy et al. 2002]) helps balance the sampling given by the parameterization.”
- Frequency response functions of shape features from full-field vibration measurements using digital image correlation (2012, methodology): “[41] suggested a stretch minimising method, nonlinear bijective mapping with minimised distance distortion, and Degener et al.”
- Surface Parameterization: a Tutorial and Survey (2005, methodology): “Numerical examples showing comparisons with discrete harmonic maps are given in [62].”
- Least squares conformal maps for automatic texture atlas generation (2002, methodology): “[27] describe an approach to minimize both atexture stretchcriterion, and texture deviation between level of details.”
- Mesh Segmentation - A Comparative Study (2006, background): “Methods that can be grouped under the first category have been presented for example in [9, 26, 6, 33], and may serve as a pre-processing for the recognition of meaningful features.”
- Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm (2005, neutral): “A u t h o r ' s p e r s o n a l”