shape compression using spherical geometry images

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Shape Compression using Spherical Geometry Images Hugues Hoppe, Microsoft Research Emil Praun, University of Utah Mesh representation irregular semi-regular completely regular What if images were represented with irregular meshes? Drawbacks:  storage of connectivity  no random lookup  rendering  compositing  filtering  compression demo Simple 2D grid Advantages:  implicit connectivity  2D lookup  raster-scan  alpha blending  DSP  JPEG 2000 Representations for media    Audio: Images: Video: uniform 1D grid uniform 2D grid uniform 3D grid  Geometry: irregular mesh historical artifact? Geometry image 2D grid sampling 3D geometry geometry image 257 x 257; 12 bits/channel Geometry image Geometry image render [r,g,b] = [x,y,z] Advantages for hardware rendering  Regular sampling  no vertex indices. Sequential traversal of source data Unified parametrization  no texture coordinates.   Main questions cut? parametrize? Construction approaches General cut Spherical Multi-chart [Gu et al. SIGGRAPH 2002] [Praun & Hoppe. SIGGRAPH 2003] [Sander et al. SGP 2003] arbitrary surface genus-zero surface  cut symmetries >1 chart  zippering Construction approaches General cut [Gu et al. SIGGRAPH 2002] arbitrary surface genus 6 Construction approaches General cut Spherical Multi-chart [Gu et al. SIGGRAPH 2002] [Praun & Hoppe. SIGGRAPH 2003] [Sander et al. SGP 2003] arbitrary surface genus-zero surface  cut symmetries >1 chart  zippering 400x160 piecewise regular Construction approaches General cut Spherical Multi-chart [Gu et al. SIGGRAPH 2002] [Praun & Hoppe. SIGGRAPH 2003] [Sander et al. SGP 2003] arbitrary surface genus-zero surface  cut symmetries >1 chart  zippering Spherical parameterization and remeshing [Praun, Hoppe 2003] Spherical parameterization and remeshing [Praun, Hoppe 2003] Spherical geometry images Steps mesh M sphere S domain D image I demo Spherical parametrization [Kent et al. 1992] [Haker et al. 2000] [Alexa 2002] [Grimm 2002] [Sheffer et al. 2003] [Gotsman et al. 2003] mesh M  sphere S Two challenges:   robustness  coarse-to-fine [Hormann et al. 1999] [Sander et al. 2001] good sampling  stretch metric [Sander et al. 2002] Coarse-to-fine algorithm Convert to progressive mesh Parametrize coarse-to-fine (maintain embedding & minimize stretch) Traditional conformal metric   Preserve angles but “area compression” Bad for sampling using regular grids Stretch metric   [Sander et al. 2001] [Sander et al. 2002] Penalizes undersampling Better samples the surface Applications of spherical remeshing Level-of-detail control     Morphing Geometry amplification Shape compression Level-of-detail control Morphing   Align meshes on the sphere. Interpolate the resulting geometry images. Geometry amplification simulation [Losasso et al. SGP 2003] “smooth geometry images” CPU GPU 33x33 65x65 129x129 257x257 floating-point geometry image + 257x257 scalar displacements demo Shape compression (Genus-zero shapes)  Spherical image topology Infinite 2D tiling Wavelets on regular 2D grid   Spherical image topology Spherical image topology Spherical image topology Infinite 2D tiling Wavelets on regular 2D grid spherical wavelets [Schröder & Sweldens 1995] image wavelets [Davis 1995] [Antonini et al 1992] Test models Compression results Compression results 90 PSNR 85 80 75 70 65 Spherical wavelets 60 55 50 45 100 1000 10000 100000 Image wavelets Globally smooth [2003] Normal mesh [2002] PGC [2000] TG [1998] File Size (bytes) Compression results 90 PSNR 85 80 75 70 65 60 Spherical wavelets 55 Image wavelets 50 45 100 1000 10000 100000 Normal mesh [2002] File Size (bytes) Compression results 90 PSNR 85 80 75 70 65 Spherical wavelets 60 55 50 45 100 1000 10000 100000 Image wavelets Globally smooth [2003] Normal mesh [2002] PGC [2000] TG [1998] File Size (bytes) Compression results 90 PSNR 85 80 75 70 65 Spherical wavelets 60 55 50 45 100 1000 10000 100000 Image wavelets Globally smooth [2003] Normal mesh [2002] PGC [2000] TG [1998] File Size (bytes) Summary  Geometry image  Simplicity of 2D grid  Applications      Rendering LOD Morphing Geometry amplification Shape compression Future work  Visual error metrics [Touma & Gotsman 1998] [Sorkine et al 2003]  Attenuation of rippling artifacts Surface boundaries Animated meshes “geometry videos” [Briceño et al 2003]  

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