Computational Geometry

1. Computational Geometry Piyush Kumar (Lecture 3: Convexity and Convex hulls) Welcome to CIS5930

2. p q p convex non-convex q Convexity A set S is convex if for any pair of points p,q S we have pq  S.

3. Convex Hulls : Equivalent definitions • The intersection of all covex sets that contains P • The intersection of all halfspaces that contains P. • The union of all triangles determined by points in P. • All convex combinations of points in P. P here is a set of input points

4. Convex Hulls • Applications • Collision Detection • Fitting shapes • Shape Approximation • NN-Searching • Useful as a preprocessing step to many other algorithms in computational geometry. • The most ubiquitous structure in computational geometry.

5. Convex hulls Extreme point Int angle < pi p6 p9 p5 p12 p7 p4 p11 p1 p8 p2 p0 Extreme edge Supports the point set

6. Convex hull : Representation • We will represent the convex hull by an enumeration of the vertices of the CH(P) in counterclockwise order. • Naïve Algorithm to compute convex hulls can be implemented in O(n3) in the plane (How?) • Anyone with an O(n2) algorithm?

7. Convex hull • has a lower bound equivalent to sorting • has many similar algorithms to sorting. • We will see today • Graham Scan • Incremental (one point at a time) • D&C • Qhull ( similar to Quick Sort) • Jarvis March • Chan’s Algorithm

8. Assignments for next week • Notes of Dr. Mount: Lec 1-4, 6 • Assignment2.cpp and ch_2.cpp • Will talk more about it towards the end of class.

9. Today (Jan 16th) • Line Segment intersection • Homeworks / Projects

10. Line Segment Intersection • Applications • VLSI (Intel uses it a lot) • Map Overlay • Clipping in Graphics • CSG • Problem : Given a set of line segments in the plane, compute all the intersection point.

11. Line Segment Intersection : GIS Cities, rivers, railroads and their overlay in western Canada.

12. Line Segment Intersection • Lower Bound from EU • EU : Given a list of n numbers, are all these numbers unique? [Y / N]? • Lower bound is Ω(nlogn) • How do we use this fact to prove a Ω(nlogn) on Line segment intersection problem? • Does this imply a lower bound of Ω(nlogn+k)? • Tell me a naïve algorithm to solve this problem.

13. Line Segment intersection • Naïve O(n^2) • Bentley Ottman sweep O((n+k)log n): 1979. • Edelsbrunner Chazelle 92 • O(nlogn +k) : Supercomplicated O(nlogn) space • Clarkson and Shor • O(nlogn +k) Randmized O(n) space • Balaban : Deterministic O(nlogn + k) in O(n space. Solved a long open problem.

14. Segment Intersection • How do we intersect two segments? • How do we implement such a primitive? CG FAQ 1.3 • Any special cases?

15. Do two segments intersect?

16. Intersection point? Solve for s, t. Beware of degenerate cases. Compute_intersection_point primitive is one of the most time consuming parts of segment intersection algorithms. For speed: Floating point filters on rational arithmetic is used.

17. Point inside Simplex?

18. Today ( Jan 20th) • Line Segment intersection Algorithm • Project discussion • Polygons

19. Line Segment intersection • Sweep line paradigm • Main idea is to sweep the entire plane with a line and compute what we want to , as we sweep past the input. • Event scheduling and updates • Carefully schedule the computation so that it does not take too much time to compute the output.

20. Line Segment Intersection • A Sorted sequence data structure • Insert • Delete • Successor/Predecessor • All in O(log n) • X-structure (or the event queue) • Y-structure (or the sweep line)

21. Plane Sweep Paradigm • Initialization: • Add all segment endpoints to the X-structure or event queue (O(nlog n)). • Sweep line status is empty. • Algorithm proceeds by inserting and deleting discrete events from the queue until it is empty.

22. Plane Sweep

23. Pretend these never happened! • No line segment is vertical • If two segments intersect, then they intersect in a single point (that is, they are not collinear). • No three line segments intersect in a common point.

24. Useful lemma • Given si,sj intersecting in p, there is a placement of the sweepline prior to this event such that si,sj are adjacent along the sweepline. • Just before an intersection occurs, the two relevant segments are adjacent to each other in the sweep line status.

25. Proof of Lemma

26. Plane Sweep • Event A: Segment left endpoint • Insert segment in sweep line or the Y-structure. • Test for intersection to the right of the sweep line with the segments immediately above and below it. Insert intersection points (if found) into X-structure or event queue. • Complexity: ? Worst case?

27. Plane Sweep : Left Endpoint

28. Plane Sweep – Algorithm • Event B: Segment right endpoint • Delete segment from sweep line status. • Test for intersection to the right of the sweep line between the segments immediately above and below. (can you do any optimization for this case? ) Insert point (if found) into event queue. • Complexity: ?

29. Plane Sweet: Right endpoint

30. A simple optimization: Right endpoints • When s is deleted, p gets recomputed. Avoid it using a hash?

31. Plane Sweep – Algorithm • Event C: Intersection point • Report the point. • Swap the two line relevant segments in the sweep line status. • For the new upper segment – test it against its predecessor for an intersection. Insert point (if found) into event queue. • Similar for new lower segment (with successor). • Complexity: O(klogn)

32. Plane sweep : Degenerate cases.

33. The Simplified Algorithm • Construct the X-structure • scan thru the X-structure (or the event queue) from left to right processing: • Segment Left endpoint • Segment right endpoint • Intersection points

34. Handling Degeneracies

35. Polygon: Definition In geometry a polygon is traditionally a planefigure that is bounded by a closed path or circuit, composed of a finite sequence of straight line segments (i.e., by a closed polygonal chain). These segments are called its edges or sides, and the points where two edges meet are the polygon's vertices or corners.

36. Point in polygon test?

37. Polygons • Point containment in simple polygon • Area of a simple polygon • Convex hull of a simple polygon • Triangulation of a simple polygon • Fast preprocessing of a convex polygon to do in/out queries.

38. Area of a convex polygon

39. Area of a polygon

40. Area of a polygon

41. From a distance

42. Area of a polyon

43. Volume in higher dimensions

44. Volume in 3D

45. Convex hull of poygons • Melkman’s Algorithm. • Uses a deque:  head-tail linked list, for which elements can be added to or removed from the front (head) or back (tail). • Considered to be the best CH algorithm for simple polygons.

46. Melkman’s algorithm • Start with CH of 3 vertices = Triangle

47. Partition plane in 4 colors • Points in the deque are circled in blue. • The point N is special point stored separately. • F and B are front and back vertices of deque.

48. Partition plane in 4 colors • Next point on the polygon can only lie in the colored regions. • Invariant : N followed by the vertices in the deque (front to back) form a convex polygon.

49. Partition plane in 4 colors • If p falls into region I, push N onto the front of the deque, then overwrite N by p. To restore the invariant, backtrack/delete vertices from the front of the deque until a convex turn is encountered.

50. Partition plane in 4 colors • If p falls into region II, push N onto the back of the deque, then overwrite N by p. Restore the invariant by backtracking/deleting vertices from the back of the deque until a convex turn is encountered.