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Transcript of Measuring Simplification Error Jonathan Cohen Computer Science Department Johns Hopkins University...
Measuring Simplification Error
Measuring Simplification Error
Jonathan Cohen
Computer Science DepartmentJohns Hopkins University
<[email protected]>http://www.cs.jhu.edu/~cohen
Why Measure Error?
• * Guide simplification process– Making better choices produces better
simplifications
• Know quality of results– Object-space error bounds describes quality
• Know when to show a particular LOD– Which LOD for a given screen-space error
• * Balance quality for large environments– What error bound for a given polygon count
Geometric Error Measures
• Promote accurate 3D shape preservation
• Also preserves screen-space shape– Silhouettes– Pixel coverage
Ford Bronco Model
TriangleTriangles: s:
41,855 41,855 27,970 27,970 20,922 20,922 12,939 12,939 8,385 8,385 4,7664,766
courtesy of Division and Viewpointcourtesy of Division and Viewpoint
Performing Simplification
• Measure cost of possible operations according to error measure– Crucial to simplification quality
• Place operations in queue according to error
• Perform operations in queue– After each operation, re-evaluate error
of operations in neighborhood
Classifying Geometric Error Metrics
• Vertex-vertex distance• Vertex-plane distance• Point-surface distance• Surface-surface distance
Vertex-Vertex Distance
• E = max( dist(v1,v3) , dist(v2,v3) ) • Measures the maximum distance
travelled by merging vertices• Appropriate during topology changes
– Rossignac and Borrel 93,– Luebke and Erikson 97
• Loose for topology-preserving collapses
vv11vv22
vv33
Minimizing/Propagating Error
• Error spheres record error at each vertex– Guaranteed bound, but very conservative
• Minimum location lies on edge– Not always at the center
Vertex-Plane Distance
• Store set of planes with each vertex– Error based on distance from vertex to planes– When vertices are merged, merge sets
• Ronfard and Rossignac 96– Store plane sets– Compute max distance
• Error Quadrics - Garland and Heckbert 96– Store quadratic form (symmetric 4x4 matrix)– Compute sum of square distances
aa bb cc
aa
bb
cc
Minimizing/Propagating Error• Minimal position found by solving
4x4 linear system• Error propagated by summing
4x4 matrices of merged vertices
Error quadrics visualized as ellipsoids (Garland/Heckbert 1997)
Memoryless Simplification• Don’t measure error from original
mesh– Measure from the current mesh– Incremental rather than total error– Lindstrom/Turk 98
• New minimization process– Preserve volume and area as
simplification progresses
• Low error demonstrated after-the-fact– Metro - Cignoni et al. 96
Point-Surface Distance
• Used in Hoppe 93 and 96• Map point set to closest points
on simplified surface• Compute sum of square
distances
Minimizing/Propagating Error• Error is minimized by solving a
linear least squares system• Propagation is unnecessary
– Error always measured from original, high-resolution points
– Slows down as more points associated with simplified mesh region
Surface-Surface Distance
• Bound maximum distance between input and simplified surfaces– Tolerance Volumes - Guéziec 96– Simplification Envelopes -
Cohen/Varshney 96– Hausdorf Distance - Klein 96– Mapping Distance - Bajaj/Schikore 96,
Cohen et al. 97
Minimizing/Propagating Error
• Minimum position need not lie on edge• Bounding volume covers surface, not
just vertices• Simplified mesh volume always
contains original mesh
Vertex-Vertex != Surface-Surface
• Error is zero at vertices and exterior edges
• Error is non-zero everywhere else– Be careful! – Not captured by vertex-
vertex or vertex-plane metrics
Edge swapEdge swap
Attribute Error Metrics
• Attributes include colors, normals, and texture coordinates
• Promote accuracy of final pixel colors
Classifying Attribute Error Metrics
• Vertex-vertex distance
• Vertex-plane distance
• Point-surface distance
• Surface-surface distance
• Image-driven metric
• Perceptually-based metric
Vertex-Vertex Distance
• GAPS point clouds - Erikson/Manocha 98
– Measure sum of square distances from vertex to its constituent vertices (area-weighted)
– Used for colors, normals, and texture coordinates
• Normal cones
– Luebke/Erikson 97, Xia et al. 97
Vertex-Plane Distance
• Higher-dimensional error quadrics– Garland and Heckbert 98
– Vertices live in higher-dimensional position + attribute space
– Planes defined in this space
• Multiple attribute quadrics– Hoppe 99
– Decouples affects of position and attributes
– Reduces storage and computational complexity
Point-Surface Distance• Extension of geometric point-surface
distance
– Hoppe 96
• Geometric correspondences found between original surface samples and simplified surface
• Sum of square attribute distances minimized
• Used primarily for vertex colors
Surface-Surface Distance
• Bajaj / Schikore 96– Geometric projections provide
local mappings– Maximum distance of scalar
attributes measured over surface
Image-driven Simplification
• Measure error by rendering– Compare resulting images– Lindstrom/Turk 2000
• Captures attribute and shading error, as well as texture content
12 cameras used to capture quality of bunny simplification (Lindstrom/Turk 2000)
Perceptually-guided Simplification• Use contrast sensitivity function to guide
simplification– Gives some handle on perceptibility– Williams et al. 2003
• Measure contrast and spatial frequency of changes induced by operation
Lit, textured model Texture contrast Lit texture contrast
Screen-space Attribute Error?
• Normal error controls dynamic refinement around highlights– Xia et al. 97, Klein 98– Doesn’t allow more simplification as
objects recede• Color control?• Texture coordinates work like
geometric error – Cohen et al. 98
• Perceptual metrics can weigh relative importance of color, normal, textures, lighting and silhouettes – for a price
Appearance Preserving Simplification
• Preserve three appearance attributes:– Surface Position– Surface Curvature– Material Color
• Each may require different sampling
v1, c1, n1
v = vertex coordinate = (x,y,z)c = color = (r,g,b)n = normal = (nx,ny,nz)
v2, c2, n2
v3, c3, n3
Traditional Polygonal Representation
Traditional Simplification
• Filters surface position, colors, and normals
• Must filter all three equally
texture map
normal map
v1, t1
v2, t2
v3, t3
v = vertex coordinate = (x,y,z)t = texture coordinate = (u,v)c = color = (r,g,b)n = normal vector = (nx,ny,nz)
c1
c2
c3
n1
n2n3
Decoupled Representation
Decoupled Approach
• Simplification filters surface position and texture coordinates
• Color and normal attributes filtered per-pixel (mip-mapping, etc.)
Normal Map vs. Bump Map
• Normal map– Absolute normals in object space– Constant as object is simplified
• Same normal map okay for all LODs
• Bump map– Perturbations of triangle normal– Changes as object is simplified
• Need different bump map for each LOD
Texture Deviation Metric
• Distance between corresponding 3D points– Same 2D texture coordinates
– Projects at run time to 2D pixel deviation
• Intuitive error tolerance specification– Pixels of deviation for both surface
position and texture error!
Point Correspondence
mesh Mmesh Mii mesh Mmesh Mi+1i+1
2D texture domain2D texture domain
(i+1)(i+1)stst edge collapse edge collapse
XXii XXi+1i+1
xxei,i+1(x) = dist(Xi, Xi+1)
Ei,i+1 = max ei,i+1(x) xP
P
Hardware Requirements
• Texture and normal (or bump) map capability– Bandwidth for attribute map lookups– Per-pixel lighting computation
• Demonstrated originally on PixelFlow
• Possible on most modern graphics hardware
Model Requirements• Parameterized model• Ideal parameterization
– Each patch fills [0,1] texture space– Allows each patch to simplify to 2 triangles– Allows equal texture resolution along patch
boundaries– Allows standard mip-mapping
• Acceptable parameterization– Each patch occupies simple polygon in
texture space– Simplification limited by polygon sides– Mip-mapping problematic
• Want to minimize required resolution
APS Level-of-detail Hierarchy
model courtesy of Stanford and Caltech
7,809 tris
3,905 tris
1,951 tris
975 tris
488 tris
Conclusions• Geometric error measured in object-space
– Used during simplification and at run-time
• Attribute error measured in attribute space– Used during simplification, and more limited use
at run-time
• Appearance-preserving simplification captures attributes and simplifies geometry– Texture deviation used during simplification and
at run-time (same as geometric error)– Attributes “simplified” using mip-mapping
References– Bajaj, Chandrajit and Daniel Schikore. Error-bounded
Reduction of Triangle Meshes with Multivariate Data. SPIE. vol. 2656. 1996. pp. 34-45.
– Cohen, Jonathan, Dinesh Manocha, and Marc Olano. Simplifying Polygonal Models using Successive Mappings. Proceedings of IEEE Visualization '97. pp. 395-402.
– Cohen, Jonathan, Marc Olano, and Dinesh Manocha. Appearance-Preserving Simplification. Proceedings of ACM SIGGRAPH 98. pp. 115-122.
– Cohen, Jonathan, Amitabh Varshney, Dinesh Manocha, Gregory Turk, Hans Weber, Pankaj Agarwal, Frederick Brooks, and William Wright. Simplification Envelopes. Proceedings of SIGGRAPH 96. pp. 119-128.
– DeFloriani, Leila, Paola Magillo, and Enrico Puppo. Building and Traversing a Surface at Variable Resolution. Proceedings of IEEE Visualization ‘97. pp. 103-110.
– Erikson, Carl and Dinesh Manocha. GAPS: General and Automatic Polygonal Simplification. Proceedings of 1999 ACM Symposium on Interactive 3D Graphics. pp. 79-88.
References– Garland, Michael and Paul Heckbert. Simplifying Surfaces
with Color and Texture using Quadric Error Metrics. Proceedings of IEEE Visualization '98. pp. 263-269, 542.
– Garland, Michael and Paul Heckbert. Surface Simplification using Quadric Error Bounds. Proceedings of SIGGRAPH 97. pp. 209-216.
– Guéziec, André. Surface Simplification with Variable Tolerance. Proceedings of Second Annual International Symposium on Medical Robotics and Computer Assisted Surgery (MRCAS '95). pp. 132-139.
– Hoppe, Hugues. Progressive Meshes. Proceedings of SIGGRAPH 96. pp. 99-108.
– Hoppe, Hugues. View-Dependent Refinement of Progressive Meshes. Proceedings of SIGGRAPH 97. pp. 189-198.
– Hoppe, Hugues, Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle. Mesh Optimization. Proceedings of SIGGRAPH 93. pp. 19-26.
– Hoppe, Hugues H. New Quadric Metric for Simplifying Meshes with Appearance Attributes. Proceedings of IEEE Visualization '99. pp. 59-66.
References– Klein, Reinhard, Gunther Liebich, and Wolfgang
Straßer. Mesh Reduction with Error Control. Proceedings of IEEE Visualization '96.
– Lindstrom, Peter and Greg Turk. Fast and Memory Efficient Polygonal Simplification. Proceedings of IEEE Visualization '98. pp. 279-286, 544.
– Lindstrom, Peter and Greg Turk. Image-Driven Simplification. ACM Transactions on Graphics. vol 19(3). pp. 204-241.
– Luebke, David and Carl Erikson. View-Dependent Simplification of Arbitrary Polygonal Environments. Proceedings of SIGGRAPH 97. pp. 199-208.
– Luebke, David, Martin Reddy, Jonathan Cohen, Benjamin Watson, Amitabh Varshney, and Robert Huebner. Level of Detail for 3D Computer Graphics. Morgan-Kaufman. 2002.
– Ronfard, Remi and Jarek Rossignac. Full-range Approximation of Triangulated Polyhedra. Computer Graphics Forum. vol. 15(3). 1996. pp. 67-76 and 462.
References– Rossignac, Jarek and Paul Borrel. Multi-
Resolution 3D Approximations for Rendering. Modeling in Computer Graphics. Springer-Verlag. 1993. pp. 455-465.
– Williams, Nathaniel, David Luebke, Jonathan Cohen, Mike Kelly, and Brenden Schubert. Perceptually-guided Simplification of Lit, Textured Meshes. Proceedings of 2003 ACM Symposium on Interactive 3D Graphics.
– Xia, Julie C., Jihad El-Sana, and Amitabh Varshney. Adaptive Real-Time Level-of-Detail-Based Rendering for Polygonal Models. IEEE Transactions on Visualization and Computer Graphics. vol. 3(2). 1997. pp. 171-183.