Geometry Images Steven Gortler Harvard University Steven Gortler Harvard University Xianfeng Gu Harvard University Xianfeng Gu Harvard University Hugues.

Slides:



Advertisements
Similar presentations
Signal-Specialized Parametrization Microsoft Research 1 Harvard University 2 Microsoft Research 1 Harvard University 2 Steven J. Gortler 2 Hugues Hoppe.
Advertisements

Lapped textures Emil Praun Adam Finkelstein Hugues Hoppe
Silhouette Clipping Pedro V. Sander Xianfeng Gu Steven J. Gortler Harvard University Pedro V. Sander Xianfeng Gu Steven J. Gortler Harvard University Hugues.
Texture-Mapping Progressive Meshes
Geometry Clipmaps: Terrain Rendering Using Nested Regular Grids
Shape Compression using Spherical Geometry Images
Surface Signals for Graphics John Snyder Researcher 3D Graphics Group Microsoft Research.
Multi-chart Geometry Images Pedro Sander Harvard Harvard Hugues Hoppe Microsoft Research Hugues Hoppe Microsoft Research Steven Gortler Harvard Harvard.
Computer graphics & visualization Real-Time Pencil Rendering Marc Treib.
Olga Sorkine and Daniel Cohen-Or Tel-Aviv University Warped textures for UV mapping encoding.
03/16/2009Dinesh Manocha, COMP770 Texturing Surface’s texture: its look & feel Graphics: a process that takes a surface and modifies its appearance using.
Surface Compression with Geometric Bandelets Gabriel Peyré Stéphane Mallat.
Consistent Mesh Parameterizations Peter Schröder Caltech Wim Sweldens Bell Labs Emil Praun Princeton.
Geometry Image Xianfeng Gu, Steven Gortler, Hugues Hoppe SIGGRAPH 2002 Present by Pin Ren Feb 13, 2003.
3D Surface Parameterization Olga Sorkine, May 2005.
Multiresolution Analysis of Arbitrary Meshes Matthias Eck joint with Tony DeRose, Tom Duchamp, Hugues Hoppe, Michael Lounsbery and Werner Stuetzle Matthias.
Xianfeng Gu, Yaling Wang, Tony Chan, Paul Thompson, Shing-Tung Yau
Inter-Surface Mapping John Schreiner, Arul Asirvatham, Emil Praun (University of Utah) Hugues Hoppe (Microsoft Research)
MATHIEU GAUTHIER PIERRE POULIN LIGUM, DEPT. I.R.O. UNIVERSITÉ DE MONTRÉAL GRAPHICS INTERFACE 2009 Preserving Sharp Edges in Geometry Images.
Consistent Spherical Parameterization Arul Asirvatham, Emil Praun (University of Utah) Hugues Hoppe (Microsoft Research)
Lapped Textures Emil Praun and Adam Finkelstien (Princeton University) Huges Hoppe (Microsoft Research) SIGGRAPH 2000 Presented by Anteneh.
Pedro V. Sander Xianfeng Gu Steven J. Gortler Harvard University
New quadric metric for simplifying meshes with appearance attributes Hugues Hoppe Microsoft Research IEEE Visualization 1999 Hugues Hoppe Microsoft Research.
Signal-Specialized Parameterization for Piecewise Linear Reconstruction Geetika Tewari, Harvard University John Snyder, Microsoft Research Pedro V. Sander,
Discontinuity Edge Overdraw
Signal-Specialized Parametrization Microsoft Research 1 Harvard University 2 Microsoft Research 1 Harvard University 2 Steven J. Gortler 2 Hugues Hoppe.
Spherical Parameterization and Remeshing Emil Praun, University of Utah Hugues Hoppe, Microsoft Research.
1 Displaced Subdivision Surfaces Aaron Lee Princeton University Henry Moreton Nvidia Hugues Hoppe Microsoft Research.
Iso-charts: Stretch-driven Mesh Parameterization using Spectral Analysis Kun Zhou, John Snyder*, Baining Guo, Heung-Yeung Shum Microsoft Research Asia.
Compressing Texture Coordinates Martin IsenburgJack Snoeyink University of North Carolina at Chapel Hill with h Selective Linear Predictions.
Cutting a surface into a Disk Jie Gao Nov. 27, 2002.
Lapped Textures Emil Praun Adam Finkelstein Hugues Hoppe Emil Praun Adam Finkelstein Hugues Hoppe Princeton University Microsoft Research Princeton University.
Bounded-distortion Piecewise Mesh Parameterization
Lapped Textures SIGGRAPH 2000 Emil Praun Adam Finkelstein Hugues Hoppe.
Surface Parametrizations Hugues Hoppe Microsoft Research IMA Workshop on Computer Graphics May 18, 2001 Hugues Hoppe Microsoft Research IMA Workshop on.
Face Fixer Compressing Polygon Meshes with Properties Martin Isenburg Jack Snoeyink University of North Carolina at Chapel Hill.
Visualization and graphics research group CIPIC January 30, 2003Multiresolution (ECS 289L) - Winter MAPS – Multiresolution Adaptive Parameterization.
Irregular to Completely Regular Meshing in Computer Graphics Hugues Hoppe Microsoft Research International Meshing Roundtable 2002/09/17 Hugues Hoppe Microsoft.
Mesh Parameterization: Theory and Practice Non-Planar Domains.
Visualization and graphics research group CIPIC Feb 18, 2003Multiresolution (ECS 289L) - Winter Progressive Meshes (SIGGRAPH ’96) By Hugues Hoppe.
The Radiosity Method Donald Fong February 10, 2004.
Consistent Parameterizations Arul Asirvatham Committee Members Emil Praun Hugues Hoppe Peter Shirley.
Smooth Geometry Images Frank Losasso, Hugues Hoppe, Scott Schaefer, Joe Warren.
1 A Novel Page-Based Data Structure for Interactive Walkthroughs Behzad Sajadi Yan Huang Pablo Diaz-Gutierrez Sung-Eui Yoon M. Gopi.
Geometry Videos Symposium on Computer Animation 2003 Hector M. Briceño Collaborators: Pedro V. Sander, Leonard McMillan, Steven Gortler, and Hugues Hoppe.
Modeling and representation 1 – comparative review and polygon mesh models 2.1 Introduction 2.2 Polygonal representation of three-dimensional objects 2.3.
Projective Texture Atlas for 3D Photography Jonas Sossai Júnior Luiz Velho IMPA.
Parameterization.
Intrinsic Parameterization for Surface Meshes Mathieu Desbrun, Mark Meyer, Pierre Alliez CS598MJG Presented by Wei-Wen Feng 2004/10/5.
Signal-Specialized Parameterization for Piecewise Linear Reconstruction Geetika Tewari, Harvard University John Snyder, Microsoft Research Pedro V. Sander,
Mesh Parameterizations Lizheng Lu Oct. 19, 2005.
Dynamic Meshing Using Adaptively Sampled Distance Fields
Adaptive Real-Time Rendering of Planetary Terrains WSCG 2010 Raphaël Lerbour Jean-Eudes Marvie Pascal Gautron THOMSON R&D, Rennes, France.
Presented By Greg Gire Advised By Zoë Wood California Polytechnic State University.
Geometric Modeling using Polygonal Meshes Lecture 1: Introduction Hamid Laga Office: South.
Geometry Images Xiang Gu Harvard University Steven J. Gortler Harvard university Hugues Hoppe Microsoft Research Some slides taken from Hugues Hoppe.
Mesh Data Structure. Meshes Boundary edge: adjacent to 1 face Regular edge: adjacent to 2 faces Singular edge: adjacent to >2 faces Mesh: straight-line.
Geometric Modeling. Volumetric o Collection device obtains regular grid of measurement values Examples: CT, MRI, PET, Ultrasound o Values are interpreted/visualized.
Computer Graphics Some slides courtesy of Pierre Alliez and Craig Gotsman Texture mapping and parameterization.
Simplifying Surfaces with Color and Texture using Quadric Error Metrics Michael Garland Paul S. Heckbert Carnegie Mellon University October 1998 Michael.
Extraction and remeshing of ellipsoidal representations from mesh data Patricio Simari Karan Singh.
Mesh Coarsening zhenyu shu Mesh Coarsening Large meshes are commonly used in numerous application area Modern range scanning devices are used.
1 Polygonal Techniques 이영건. 2 Introduction This chapter –Discuss a variety of problems that are encountered within polygonal data sets The.
Euler characteristic (simple form):
Reverse Engineering of Point Clouds to Obtain Trimmed NURBS Lavanya Sita Tekumalla Advisor: Prof. Elaine Cohen School of Computing University of Utah Masters.
Surface Signals for Graphics
Surface parametrizations
Meshes.
Mesh Parameterization: Theory and Practice
Chap 10. Geometric Level of Detail
Presentation transcript:

Geometry Images Steven Gortler Harvard University Steven Gortler Harvard University Xianfeng Gu Harvard University Xianfeng Gu Harvard University Hugues Hoppe Microsoft Research Hugues Hoppe Microsoft Research

Irregular meshes Vertex 1 x 1 y 1 z 1 Vertex 2 x 2 y 2 z 2 … Face Face …

Texture mapping Vertex 1 x 1 y 1 z 1 Vertex 2 x 2 y 2 z 2 … s1 t1s1 t1s2 t2s2 t2s1 t1s1 t1s2 t2s2 t2 normal map s t Face Face …

Complicated rendering process Vertex 1 x 1 y 1 z 1 Vertex 2 x 2 y 2 z 2 … random access! s1 t1s1 t1s2 t2s2 t2s1 t1s1 t1s2 t2s2 t2 Face Face … ~40M Δ/sec

Semi-regular representations irregular vertex indices only semi-regular [Eck et al 1995] [Lee et al 1998] [Khodakovsky 2000] [Guskov et al 2000] …

Geometry Image geometry image 257 x 257; 12 bits/channel 3D geometry completely regular sampling

Basic idea demo cut parametrize

Basic idea cut sample

cut [r,g,b] = [x,y,z] render store

How to cut ? sphere in 3D 2D surface disk

How to cut ? l Genus-0 surface  any tree of edges sphere in 3D 2D surface disk

How to cut ? l Genus-g surface  2g generator loops minimum torus (genus 1)

Surface cutting algorithm (1) Find topologically-sufficient cut: 2g loops [Dey and Schipper 1995] [Erickson and Har-Peled 2002] (2) Allow better parametrization: additional cut paths [Sheffer 2002] (1) Find topologically-sufficient cut: 2g loops [Dey and Schipper 1995] [Erickson and Har-Peled 2002] (2) Allow better parametrization: additional cut paths [Sheffer 2002]

Step 1: Find topologically-sufficient cut (a) retract 2-simplices (b) retract 1-simplices

Results of Step 1 genus 6 genus 0 genus 3

Step 2: Augment cut l Make the cut pass through “extrema” (note: not local phenomena). l Approach: parametrize and look for “bad” areas. l Make the cut pass through “extrema” (note: not local phenomena). l Approach: parametrize and look for “bad” areas.

Step 2: Augment cut …iterate while parametrization improves

Results of Steps 1 & 2 genus 1 genus 0

Parametrize boundary Constraints: n cut-path mates identical length n endpoints at grid points Constraints: n cut-path mates identical length n endpoints at grid points a a’ a a’  no cracks

Parametrize interior –optimizes point-sampled approx. [Sander et al 2002] n Geometric-stretch metric –minimizes undersampling [Sander et al 2001] n Geometric-stretch metric –minimizes undersampling [Sander et al 2001]

Previous metrics (Floater, harmonic, uniform, …) Stretch parametrization

SampleSample geometry image

RenderingRendering (65x65 geometry image)

rendering geometry image x 12b/ch normal-map image x 8b/ch Rendering with attributes

Advantages for hardware rendering l Regular sampling  no vertex indices. l Unified parametrization  no texture coordinates.  Raster-scan traversal of source data: geometry & attribute samples in lockstep. Summary: compact, regular, no indirection Summary: compact, regular, no indirection l Regular sampling  no vertex indices. l Unified parametrization  no texture coordinates.  Raster-scan traversal of source data: geometry & attribute samples in lockstep. Summary: compact, regular, no indirection Summary: compact, regular, no indirection

normal map 512x512; 8b/ch Normal-Mapped Demo geometry image 129x129; 12b/ch demo

color map 512x512; 8b/ch Pre-shaded Demo geometry image 129x129; 12b/ch

ResultsResults 257x257 normal-map 512x512

ResultsResults 257x257 color image 512x512

Mip-mappingMip-mapping 257x257129x12965x65 boundary constraints set for size 65x65

Hierarchical culling view-frustum culling backface culling geometry image normal-map image

CompressionCompression  1.5 KB + topological sideband (12 B) fused cut 295 KB Image wavelet-coder

Compression results 1.5 KB 3 KB 12 KB 49 KB 295 KB 

Rate distortion

Some artifacts aliasing anisotropic sampling

SummarySummary l Simple rendering: compact, no indirection, raster-scan stream. l Mipmapped geometry l Hierarchical culling l Compressible l Simple rendering: compact, no indirection, raster-scan stream. l Mipmapped geometry l Hierarchical culling l Compressible

Future work l Better cutting algorithms l Feature-sensitive remeshing l Tangent-frame compression l Bilinear and bicubic rendering l Build hardware l Better cutting algorithms l Feature-sensitive remeshing l Tangent-frame compression l Bilinear and bicubic rendering l Build hardware