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VISUALIZING SURFACES CS 519 Dr. Lawrence Angrave Dr. Eric Shaffer Surfaces • Intuitively surface is a boundary of a solid – More formally: Orientable continuous 2D manifold embedded in R3 – That’s probably overly restrictive • Today we will look at – Sources of surface data – Surface representations – Common surface processing operations Sources of Surface Data • Manual input… • Writing a program to create the data – Procedural modeling • Extraction from volume data • Use of a modeling program – Can be surface or solid-based modeling • Reconstruction from image data – Called photogrammetry • From 3D scanning technology Extraction from volume data • Volume data acquired via – Simulation – Scanning (MRI, CT-Scan, etc.) • Algorithmically find surfaces of constant value – For MRI that would be density… • We’ll talk more about this during the volume rendering lectures Moldeling Software • Surface modeling – Generate a surface model for a simulation – Lots of options (Gridgen™, etc) Modeling Software • Solid Modeling – CAD tools – Processes mimic machining (drilling, cutting). • Typically have a faceter that converts solid model to polygons • Interactive visualization requires fast rendering – FEM faceters often over-tessellate Surface Modeling in VTK vtkLinearExtrusionFilter • Creates a swept surface vtkRotationalExtrusionFilter • Creates a surface of rotation vtkTubeFilter • Models a generalized cylinder around a chain of line segments vtkRibbonFilter • Creates strips along line segments perpendicular to normals stored per-segment 3D Scanning St. Matthew David Forma Urbis Romae Scanners used in the Digital Michelangelo Project Cyberware Faro + 3D Scanners Cyra - for statues - for tight spots - for architecture Scanning St. Matthew working in scanning scanning the museum geometry color Problems in 3D scanning 1. optically uncooperative materials 2. scanning in the presence of occlusions 3. filling holes in dense polygon models 4. handling large datasets How optically cooperative is marble? [Godin et al., Optical Measurement 2001] • systematic bias of 40 microns • noise of 150 – 250 microns – worse at oblique angles of incidence – worse for polished statues Hard problem : scanning in the presence of occlusions < 30° ? 20° ? ? • occlusions (& self-occlusion) force grazing scans, which lead to holes • a scanner with a fixed triangulation angle cannot circumvent all occlusions • a hammer & chisel can reach places a triangulation scanner cannot filling holes in dense polygon models signed distance function volumetri diffusion process The importance of subsurface scattering BRDF BSSRDF [Jensen et al., Siggraph 2000] Hard problem: handling large datasets Scanning the David • 480 individually aimed scans • 2 billion polygons • 7,000 color images • 32 gigabytes • 30 nights of scanning • 22 people LiDAR: Light Detection and Ranging • Range based on time for laser reflection to return How do we go from points to a surface? You can generate a volumetric representation and extract a surface…like you’ll see in a few weeks. 2-D Meshing vtkDelaunay2D • Input is a 2-D set of points stored in a vtkPolyData • Connects 3 points into a triangle if no other point lies in circumcircle • Mesh avoids any faces in input (source) accompanying points in vtkPolyData 3-D Meshing vtkDelaunay3D • Triangulates points in 3-D based on tets and circumsphere vtkSurfaceReconstructionFilter • Implements Hoppe S92 & Ph.D. • Distance to vertex i’s tangent plane fi(x) = (x – vi) · ni • Local least squares provides ni • Implicit returns min distance • Polygonize Surface Representations 2D example Surface Representations • Parametric surfaces – Tensor-product spline surfaces (NURBS) • Standard in many CAD systems – Piecewise polynomial surface – Connected in continuous manner Some Drawbacks of Tensor- Product Surfaces • Modeling a complicated surface in a smooth manner requires lots of patches and lots of extra constraints • Some operations (refinement) difficult to implement. • Modeling sharp features is and holes requires extra work Polygonal Meshes • Most common type of surface model • A collection of polygons typically sharing vertices • Very versatile, can model complex geometries • Rendering hardware optimized for processing triangle meshes – Graphics libraries generally tessellate non- polygonal mesh representations to render them VTK Cell Types Triangle Mesh Data Structures • “Polygon Soup” or “Face Set” • No explicit connectivity information • Implemented in the STL file format 36B per triangle ..or 72B per vertex (V-E+F=2) Drawbacks? Triangle Mesh Data Structures • “Indexed Face Set” • OBJ, PLY format • Table of vertices • Table of faces using vertex indices • 36B per vertex • Drawbacks? Triangle Mesh Data Structure • Half-Edge • Table of vertices: position, outgoing half-edge • Table of Faces: incident half-edge • Table of Half-Edges: origin vertex, face, opposite half-edge, prev half-edge, next half-edge • Muller, D. E. ; Preparata, F. P. "Finding the Intersection of Two Convex Polyhedra", Tech. Rept. UIUC, 1977, 38pp, also Theoretical Computer Science", Vol. 7, 1978, 217-236 Triangle Mesh Data Structure • Half-Edge • 144B per vertex • Operations: – Walk around the boundary of a given face – Visit all edges incident to a vertex v – Visit all vertex neighbors of v – How fast? • Drawbacks? • Supported by CGAL and OpenMesh Half-Edge example Rendering Efficiency in VTK vtkTriangleFilter • Converts polygonal mesh into a triangle mesh vtkStripper • Collects triangles into strips where each new vertex leads to a new triangle vtkCleanPolyData • Merges duplicate vertices and removed degenerate cells vtkQuantizePolyData • Removes unnecessary precision due to e.g. noise Geometry Processing for Visualization • Simplification/Level-of-Detail generation • Feature Detection • Mesh Repair Mesh Simplification •Visualization commonly confronts the problem of overwhelming amounts of data and/or geometry •Need methods that balance the loss of data fidelity with realities of •Visual clutter •Memory constraints •Interactive framerates •Generates level-of-detail models •Coarsely meshed distant objects •Finely meshed near objects Formalizing Surface Simplification Problem • Produce approximations with fewer triangles – Computationally efficient – Should be visually as similar to original as possible – In practice similarity of shape used instead Vertex Clustering Partition space into cells • grids, spheres, octrees Merge all vertices in the same cell • triangles with multiple vertices in a cell degenerate Vertex Decimation vtkDecimate • Implements Schroeder et al. “Decimation of Triangle Meshes” S92 • Removes a vertex if: 1. It is near the average plane of the faces sharing it 2. None of its adjacent edges is greater than a “feature” angle 3. Its removal doesn’t change the surface topology • Retriangulate the resulting polygon • Repeat until target size or error reached How to Create Level-of- detail (LOD) distance from viewer? close far 10,000 2,000 1,000 500 250 Concern: transitions may “pop” would like smooth LOD Progressive Meshes • Proposed by Hoppe Siggraph 96 • Idea: apply sequence of edge collapses: ecol(vs ,vt , vs ) ’ vt vl vr vl vr ’ vs vs (optimization) S Invertible! Vertex split transformation: attributes vspl(vs ,vl ,vr , vs ,vt ’,…)’ vt’ vl vs vr vl vr vs’ S Simplification process 13,546 500 152 150 ^ M=Mn M175 M1 M0 ecoln-1 ecoli ecol0 Progressive Meshes Progressive Meshes Progressive Meshes 152 vtkDecimatePro • Implements Hoppe S96 • Simplification – Vertices sorted into priority queue according to decimation error 500 – Vertices decimated in this order until single triangle remains • Streaming mesh transmission – Insert vertices in reverse order – Unlike edge-collapse/vertex-split, vertex removal/insertion not continuous 13,546 Quadric Error Metric v v v 1 2 ’ vtkQuadricDecimation Q Qi Q j ( A i A j , bi b j , ci c j ) • Garland & Heckbert, Q( v ') v A b S97 v n d Error( v ) (n i v di ) T i Q ( A , b, c ) (nnT, dn, d ) Q( v ) vTAv bTv c (n v di ) Qi ( v ) Qi ( v ) T i i i i QEM on Attributes • Hoppe Vis99 • Use quadrics to sense variance not only in vertex position, but other attributes: – Color – Texture coord. – Scalar/vector data 50K tris 10K tris Feture Detection Critical procedure in many meshing tasks Identifies edges that are design features e.g. edges to be preserved during coarsening Automation saves significant person-hours Manual identification simply infeasible for very detailed meshes No commercial code with equal capabilities Feature Detection Technical Details: Eigen-analysis • Eigenvalues of covariance matrix at each vertex – Contain info about principal curvatures – Unified asymptotic analysis and singularity analysis – Automatic computation of thresholds for eigenvalues from angle thresholds • Eigenvectors contain info about principal directions – Relate to direction of feature edges – Invaluable for determining connectivity of feature curves • X. Jiao and N.R. Bayyana, Identification of C1 and C2 Discontinuities for Surface Meshes in CAD, Computer-Aided Design, Vol 40, pages 160-175, 2008. smooth:1 ridge: 2 corner: 3 large ev. large ev. large ev. Eigenvalues of the covariance matrix…. • Covariance matrix Q for a set of n points x1,..,xn – Let p be the average of the n points – Q=[qij] 1 n qij (x ik pi )( x jk p j ) n k 1 • Eigenvalues are all scalars λ such that Qv=λv Robust Vertex-Edge Dual Approach for Feature Detection Input: mesh, angle thresholds Pre-classify vertices and edges using eigenvalue analysis Re-classify vertices based on incident ridge edges Connect feature curves along ridge edges and ridge vertices If changed Filter open-ended weak feature curves If no change Output: ridge edges, vertex classification Sample results Noisy nozzle region due to coarse mesh Curved thin wall with skinny triangles Feature Detection vtkFeatureEdges • Extracts features from meshes • Finds edges that are: – Boundary (one face) – Features (dihedral < feature ) – Non-manifold (3+ faces) Curvature vtkCurvature • Computes curvatures r as vertex attribute • Gaussian curvature k = 1/r K(v) = k1 k2 = 2p – Suvw uvw (weighted by Area(uvw)/3) • Mean curvature H(v) = (k1 + k2)/2 = S<uv> dihedral(<uv>) (weighted by ||<uv>||) Meyer, Desbrun, Schroder, Barr Curvature Curvature is rate of change of tangent vectors along a curve. Principal curvatures: k1 is max curvature at p k2 is min curvature at p Principal directions are the associated tangent vectors Mesh Repair • Goal: – Produce a manifold triangle mesh – Possibly without boundary (i.e. watertight) – Possibly remove topological “noise” – Provide consistent normal orientation Mesh Artifacts Mesh Artifacts • Holes and islands in meshes from range images Mesh Artifacts • Contoured meshes are extracted from volume data – Often have topological noise Mesh Artifacts • Triangulated spline surfaces often have gaps and overlaps Mesh Artifacts • Geometrical noise – Fixed by smoothing (next lecture) • “poor quality” in the FEM sense – Fixed by optimization or remeshing (different class) Hole-filling • vtkFillHolesFilter – Surface-based approach – Identifies boundary loops and triangulates Surface-Based Hole-filling Issues • Need to refine and smooth the result – Otherwise geometry won’t match • Islands not incorporated • Self-intersections can occur • Volumetric approaches preferable – Simplification and repair of polygonal models using volumetric techniques [Noorudin & Turk] – Automatic restoration of polygon models[Bischoff, Pavic, Kobbelt 2005] If you were reviewing a paper you would be asked: • What is the main contribution of the paper? • How important is it? • Is it practical to implement? • Is the paper understandable? • What are the drawbacks or shortcomings of the work described in the paper? And as a researcher you should ask your yourself: How could I improve upon this work? Acknowledgments • Some material used courtesy of: – Hugues Hoppe • http://research.microsoft.com/en-us/um/people/hoppe/ – Stanford Digital Michelangelo Project • http://graphics.stanford.edu/projects/mich/ – M. Bosch, et al • http://www.pmp-book.org/ – Michael Garland • http://mgarland.org/

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