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CSE 554 Lecture 7: Simplification

Learn about fairing, vertex relocation, and simplification methods in geometry processing, essential for smoother appearance in animations and simulations. Understand vertex count reduction and optimization problems.

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CSE 554 Lecture 7: Simplification

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  1. CSE 554Lecture 7: Simplification Fall 2016

  2. Geometry Processing • Fairing (smoothing) • Relocating vertices to achieve a smoother appearance • Method: centroid averaging • Simplification • Reducing vertex count • Deformation • Relocating vertices guided by user interaction or to fit onto a target

  3. Why do we care? • Polygon count is a major performance factor • Animation (deformation), rendering, simulation, interaction Space shuttle aerodynamics (NASA) Character animation (Pixar) Ray tracing rendering (POV-Ray)

  4. Simplification (2D) • Replacing a 2D polygon by another with fewer vertices • Preserves the shape and topology of the input polygon

  5. Simplification (2D) • An optimization problem: • Given an input polygon and a target vertex count N • Find the coordinates of N vertices that minimize the distance from the new polygon formed by these vertices and the input polygon 200 vertices 50 vertices

  6. Simplification (2D) • A greedy heuristic: • Iteratively merges two neighboring vertices into one, until the target vertex count is reached • Each merging minimizes the error to the original polygon 200 vertices 50 vertices

  7. After replacement: Simplification (2D) • If I want to replace two vertices with one, where should it be?

  8. After replacement: Simplification (2D) • If I want to replace two vertices with one, where should it be? • Shortest distances to the supporting lines of involved edges

  9. Points and Vectors • Same representation • Different meaning: • Point: a fixed location (relative to {0,0} or {0,0,0}) • Vector: a direction and magnitude • No location (any location is possible) Y 2 x 1 2

  10. Point Operations • Subtraction • Result is a vector • Addition with a vector • Result is a point • Can points add?

  11. Adding Points • Affine combinations • Weighted addition of points where all weights sum to 1 • Result is another point • Same as adding scaled vectors to a point

  12. Adding Points • Affine combinations: examples • Mid-point of two points • Linear interpolation of two points • Centroid of multiple points

  13. Vector Operations • Addition/Subtraction • Result is a vector • Scaling by a scalar • Result is a vector • Magnitude • Result is a scalar • A unit vector: • To make a unit vector (normalization):

  14. More Vector Operations • Dot product (in both 2D and 3D) • Result is a scalar • In coordinates (simple!) • 2D: • 3D: • Matrix product between a row and a column vector

  15. h More Vector Operations • Uses of dot products • Angle between vectors: • Orthogonal test: • Projected length of onto :

  16. More Vector Operations • Cross product (only in 3D) • Result is another 3D vector • Direction: Normal to the plane where both vectors lie (right-hand rule) • Magnitude: • In coordinates:

  17. More Vector Operations • Uses of cross products • Getting the normal vector of the plane • E.g., the normal of a triangle formed by • Computing area of the triangle formed by • Testing if vectors are parallel:

  18. Properties (Sign change!)

  19. Simplification (2D) • Distance to a line • Line represented as a point q on the line, and a perpendicular unit vector (the normal) n • To get n: take a vector {x,y} along the line, n is {-y,x} followed by normalization • Distance from any point p to the line: • Projection of vector (p-q) onto n • This distance has a sign • “Above” or “under” of the line • We will use the distance squared

  20. Simplification (2D) • Closed point to multiple lines • Sum of squared distances from p to all lines (Quadratic Error Metric, QEM) • Input lines: • We want to find the p with the minimum QEM • Since QEM is a convexquadratic function of p, the minimizing p is where the derivative of QEM is zero, which is a linear equation

  21. Row vector Matrix transpose [Eq. 1] Matrix (dot) product 2x2 matrix 1x2 column vector Scalar Simplification (2D) • Minimizing QEM • Writing QEM in matrix form

  22. Simplification (2D) • Minimizing QEM • Solving the zero-derivative equation: • A linear system with 2 equations and 2 unknowns (px,py) • Using Gaussian elimination, or matrix inversion: [Eq. 2]

  23. Simplification (2D) • What vertices to merge first? • Pick the ones whose replacement vertex introduces least QEM error(usually lies in flat areas)  

  24. Simplification (2D) • The algorithm • Step 1: For each edge, compute the best vertex location to replace that edge, and the QEM at that location. • Store that location (called minimizer) and its QEM with the edge.

  25. Simplification (2D) • The algorithm • Step 1: For each edge, compute the best vertex location to replace that edge, and the QEM at that location. • Store that location (called minimizer) and its QEM with the edge. • Step 2: Pick the edge with the lowest QEM and collapse it to its minimizer. • Update the minimizers and QEMs of the re-connected edges.

  26. Simplification (2D) • The algorithm • Step 1: For each edge, compute the best vertex location to replace that edge, and the QEM at that location. • Store that location (called minimizer) and its QEM with the edge. • Step 2: Pick the edge with the lowest QEM and collapse it to its minimizer. • Update the minimizers and QEMs of the re-connected edges.

  27. Simplification (2D) • The algorithm • Step 1: For each edge, compute the best vertex location to replace that edge, and the QEM at that location. • Store that location (called minimizer) and its QEM with the edge. • Step 2: Pick the edge with the lowest QEM and collapse it to its minimizer. • Update the minimizers and QEMs of the re-connected edges. • Step 3: Repeat step 2, until a desired number of vertices is left.

  28. Simplification (2D) • The algorithm • Step 1: For each edge, compute the best vertex location to replace that edge, and the QEM at that location. • Store that location (called minimizer) and its QEM with the edge. • Step 2: Pick the edge with the lowest QEM and collapse it to its minimizer. • Update the minimizers and QEMs of the re-connected edges. • Step 3: Repeat step 2, until a desired number of vertices is left.

  29. Simplification (2D) • Step 1: Computing minimizer and QEM on an edge • Consider supporting lines of this edge and adjacent edges • Compute and store at the edge: • The minimizing location p (Eq. 2) • QEM (substitute p into Eq. 1) • Used for edge selection in Step 2 • QEM coefficients (a, b, c) • Used for fast update in Step 2 Stored at the edge: [Eq. 1]

  30. Simplification (2D) • Step 2: Collapses and updates • Remove the edge and its vertices • Re-connect two neighbor edges to the minimizer of the removed edge • For each re-connected edge: • Increment its coefficients by that of the removed edge • The coefficients are additive! • Re-compute its minimizer and QEM Collapse : new minimizer locations computed from the updated coefficients

  31. Simplification (3D) • The algorithm is similar to 2D • Replace two edge-adjacent vertices by one vertex • Placing new vertices closest to supporting planes of adjacent triangles • Prioritize collapses based on QEM

  32. Simplification (3D) • Distance to a plane (similar to the line case) • Plane represented as a point q on the plane, and a unit normal vector n • For a triangle: n is the cross-product of two edge vectors • Distance from any point p to the plane: • Projection of vector (p-q) onto n • This distance has a sign • “above” or “below” the plane • We use its square

  33. 3x3 matrix 1x3 column vector Scalar Simplification (3D) • Closest point to multiple planes • Input planes: • QEM (same as in 2D) • In matrix form: • Find p that minimizes QEM: • A linear system with 3 equations and 3 unknowns (px,py,pz)

  34. Simplification (3D) • Step 1: Computing minimizer and QEM on an edge • Consider supporting planes of all triangles adjacent to the edge • Compute and store at the edge: • The minimizing location p • QEM[p] • QEM coefficients (a, b, c) The supporting planes for all shaded triangles should be considered when computing the minimizer of the middle edge.

  35. Simplification (3D) Degenerate triangles after collapse • Step 2: Collapsing an edge • Remove the edge with least QEM • Re-connect neighbor triangles and edges to the minimizer of the removed edge • Remove “degenerate” triangles • Remove “duplicate” edges • For each re-connected edge: • Increment its coefficients by that of the removed edge • Re-compute its minimizer and QEM Duplicate edges after collapse Collapse

  36. Simplification (3D) • Example: 5600 vertices 500 vertices

  37. Further Readings • Fairing: • “A signal processing approach to fair surface design”, by G. Taubin (1995) • No-shrinking centroid-averaging • Google citations > 1000 • Simplification: • “Surface simplification using quadric error metrics”, by M. Garland and P. Heckbert (1997) • Edge-collapse simplification • Google citations > 2000

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