2. Polygon overlay in double precision arithmetic One example of why robust geometric code is hard to write Jack Snoeyink & Andrea Mantler Computer Science, UNC Chapel Hill

3. Outline n Motivating problems –Clipping, polygon ops, overlay, arrangement –Study precision required by algorithm n Quick summary of algorithms –Test pairs, sweep, topological sweep n A double-precision sweep algorithm –“Spaghetti” segments n Conclusions

4. Three problems in the plane n Polygon clipping (graphics) n Boolean operations (CAD) n Map overlay (GIS)

5. Build red/blue arrangement n Build the arrangement of: n red and n blue line segments in plane, specified by their endpoint coordinates & having no red/red or blue/blue crossings.

6. Why precision is an issue n Algorithms find geometric relationships from coordinate computations. n Efficient algorithms compute only a few relationships and derive the rest.

7. Assumptions & Goal n Assumptions for correctness –Solve the exact problem given by the input –Work for any distribution of the input n Goal: Input+output sensitive algorithm –Demand least precision possible –O(n log n + k) for n segs, k intersections

8. Algorithms to build line segment arrangements n Brute force: test all pairs n Sweep the plane with a line [BO79,C92] n Topological sweep [CE92] n Divide & Conquer [B95] n Trapezoid sweep [C94]

9. Four geometric tests n Orientation/Intersection test n Intersection-in-Slab n Order along line n Order by x coordinate

10. Four geometric tests n Orientation/Intersection test n Intersection-in-Slab n Order along line n Order by x coordinate

11. Four geometric tests n Orientation/Intersection test n Intersection-in-Slab n Order along line n Order by x coordinate

12. Four geometric tests n Orientation/Intersection test n Intersection-in-Slab n Order along line n Order by x coordinate

13. Algorithms to build line segment arrangements n Brute force: test all pairs n Sweep the plane with a line [BO79,C92] n Topological sweep [CE92] n Divide & Conquer [B95]

14. Brute force n Test all pairs for intersection

15. n Sort along lines n Break&rejoin segs Brute force

16. Plane sweep [BO79,C92] n Maintain order along sweep line

17. Plane sweep [BO79,C92] n Maintain order along sweep line

18. Plane sweep [BO79,C92] n Maintain order along sweep line n Know all intersections behind n Next event queue

19. Plane sweep [BO79,C92] n Maintain order along sweep line n Know all intersections behind n Next event queue

20. n Maintain order along sweep line n Know all intersections behind n Next event queue Plane sweep [BO79,C92]

21. n Maintain order along sweep line n Know all intersections behind n Next event queue

22. Plane sweep [BO79,C92] n Maintain order along sweep line n Know all intersections behind n Next event queue

23. Plane sweep [BO79,C92] n Maintain order along sweep line n Know all intersections behind n Next event queue

24. Plane sweep [BO79,C92] n Maintain order along sweep line n Know all intersections behind n Next event queue

25. Topological sweep [CE92] n Maintain order along sweep curve n Know all intersections behind

26. Topological sweep [CE92] n Maintain order along sweep curve n Know all intersections behind n “20 easy pieces”

27. Divide and Conquer [B95] n Find intersections in slab with staircase

28. Divide and Conquer [B95] n Find intersections in slab with staircase n Remove staircase

29. Divide and Conquer [B95] n Find intersections in slab with staircase n Remove staircase n Partition & repeat

30. Degrees of predicates n Orientation/Intersection test (deg. 2) n Intersection-in-Slab (deg. 3) n Order along line (deg. 4) n Order by x coordinate (deg. 5)

31. Degree computations Point p = ( 1,px,py ) = ( (0),(1),(1) ) n Line equation through points p, q:

32. Degree computations Point p = ( 1,px,py ) = ( (0),(1),(1) ) Line equation: (2)W+(1)X+(1)Y n Point at intersection of two lines:

33. Degree computations Point p = ( 1,px,py ) = ( (0),(1),(1) ) Line equation: (2)W+(1)X+(1)Y Point at intersection ( (2),(3),(3) )  Orientation Test: (2)(0)+(1)(1)+(1)(1) = (2)  In Slab: (1) < (3)/(2) or (2)(1) < (3) = (3)  Same Line: (2)(2) + (1)(3) + (1)(3) = (4)  x Order: (3)/(2) < (3)/(2) or (5)<(5) = (5)

34. Degree of algorithms to build line segment arrangements n Brute force (2/4) n Sweep with a line (5) n Topological sweep (4) n Divide & Conquer (3/4) n Trapezoid sweep (3) Restrict to double precision

35. Restricted predicates imply... n Restricted to double precision: –Can’t test where an intersection is –Can’t sort on lines –Can’t sort by x

36. Spaghetti lines n Restricted to double precision: –Can’t test where an intersection is –Can’t sort on lines –Can’t sort by x

37. Spaghetti lines n Restricted to double precision: –Push segments as far right as possible

38. Spaghetti lines n Restricted to double precision: –Push segments as far right as possible –Endpoints witness intersections

39. A sweep for red/blue spaghetti n Maintain order along sweep consistent with pushing intersections to right n Detect an intersection when the sweep passes its witness

40. Data Structures n Sweep line:

41. Data Structures n Sweep line: –Alternate bundles of red and blue segs

42. Data Structures n Sweep line: –Alternate bundles of red and blue segs –Bundles are in doubly-linked list

43. Data Structures n Sweep line: –Alternate bundles of red and blue segs –Bundles are in doubly-linked list –Blue bundles are in a balanced tree

44. Data Structures n Sweep line: –Alternate bundles of red and blue segs –Bundles are in doubly-linked list –Blue bundles are in a balanced tree –Each bundle is a small tree

45. Events n Sweep events –Now only at vertices n Processing

46. Event processing n Sweep events –Now only at vertices n Processing red –Use trees to locate point in blue bundle

47. Event processing n Sweep events –Now only at vertices n Processing red –Use trees to locate point in blue bundle –Use linked list to locate in red

48. Event processing n Sweep events –Now only at vertices n Processing red –Use trees to locate point in blue bundle –Use linked list to locate in red –Split/merge bundles to restore invariant

49. Event processing n Sweep events –Now only at vertices n Processing red –Use trees to locate point in blue bundle –Use linked list to locate in red –Split/merge bundles to restore invariant

50. Event processing n Process blue the same n Time: O(n log n + k) –Tree operations prop to # of vertices –Bundle operations prop to # of intersections –Degeneracies can easily be handled

51. Handling degeneracies n Shared endpoints n Endpoint on a line n Collinear segments

52. Handling degeneracies n Shared endpoints n Endpoint on a line n Collinear segments Introduce vertices, since they are exact

53. Algorithm Summary n We have an optimal algorithm to build an arrangement of red/blue segments, (and only for red/blue segments). n We used only the orientation predicate. n Data structuring is moderate. n Can handle point/line degeneracies: breaking lines at input points is OK.

54. Demo applet www.cs.unc.edu/ ~snoeyink/ demos/ rbseg/

55. A lower bound for spaghetti With only intersection and above/below tests, counting intersections requires Ω(nk ½ ) ops. With convex hulls O(n log 2 n + k) ops suffice for intersections [BS99], but not arrangement. n k½k½

56. Open question n How do we perform geometric rounding to take output back to single precision? –probably dependant on application domain –snap rounding is one idea

57. Fin