Surface reconstruction from point clouds

1. Surface reconstruction from point clouds Based on: Surface reconstruction, by Nina Amenta, Section 4.1 in: Point-Based Graphics Slides by Marc van Kreveld for DDM

2. Surface reconstruction • Converting a point cloud in a more explicit representation, like a triangle mesh • Needs a dense enough point set • especially in areas with much detail • especially in areas with high curvature • Sometimes outliers must be dealt with • Many different approaches

3. Surface reconstruction • Often begins with normal estimation at all points • Basic method types: • Implicit surface methods • Voronoi/Delaunay methods • Surface evolution methods

4. Implicit surface methods

5. Implicit surface methods • Voxel-based method of Hoppe et al. (1992) • Estimate normal at each point p of the set P • Make all normals point outwards • Define f(x) to be the distance to the tangent plane of p, where p is the point of P closest to x • Use marching cubes to extract the zero-set of f

6. Implicit surface methods • Voxel-based method of Hoppe et al. (1992) • Estimate normal at each point p of the set P • Make all normals point outwards • Define f(x) to be the distance to the tangent plane of p, where p is the point of P closest to x • Use marching cubes to extract the zero-set of f within the 3D Voronoi cell of p, the surface is the tangent plane at p !

7. Implicit surface methods • Voxel-based method of Hoppe et al. (1992) • Estimate normal at each point p of the set P • Make all normals point outwards • Define f(x) to be the distance to the tangent plane of p, where p is the point of P closest to x • Use marching cubes to extract the zero-set of f within the 3D Voronoi cell of p, the surface is the tangent plane at p ! f is a discontinuous function !

13. Implicit surface methods • Exact computation of the zero-set of f involves computing the 3D Voronoi diagram of P and intersecting each cell with the local tangent plane • Hoppe et al. use marching cubes which can take care of many of the (smaller) gaps between pieces of surface

14. Implicit surface methods • Marching cubes algorithm (Lorensen and Cline, 1987) • method to determine the level-0 set of an implicit function • uses a grid of cubes and evaluates the function only at the corners • from the eight corners of each cube and their status inside/outside (function is negative/positive), a piece of surface is made inside the cube

15. Implicit surface methods • Marching squares (for level-5 set) • Four corners give 16 cases; twoare ambiguous all inside all outside

16. Implicit surface methods • Locally, we cannot know how to resolve an ambiguous case

17. Implicit surface methods • Marching cubes is the 3D equivalent idea • Eight corners of the square give 256 cases, many of which are symmetric or inverted 15 cases remain

19. Implicit surface methods • Two cubes that share an ambiguous facet must use compatible solutions: imagine case 3 (2 red and 6 blue vertices) and its inverted form (6 red and 2 blue vertices) share an ambiguous facet

20. Implicit surface methods • A solution was given by Nielson and Hammann (1991), called asymptotic decider • It includes different ways to treat certain inverted forms (so they are not inverted but new cases)

21. Implicit surface methods

22. Implicit surface methods • Instead of choosing the surface to go through the middle of an edge with an inside and an outside endpoint, it is better to use interpolation (linear) f(.) = –2 let surface intersect the edge here f(.) = 5

23. Implicit surface methods • Back to Hoppe et al.’s voxel-based method and how marching cubes (squares) applies • Estimate normal at each point p of the set P • Make all normals point outwards • Define f(x) to be the distance to the tangent plane of p, where p is the point of P closest to x • Use marching cubes to extract the zero-set of f

24. Implicit surface methods

25. Implicit surface methods

26. Implicit surface methods

27. Implicit surface methods

28. Implicit surface methods • Hoppe et al.’s method resolves the gaps resulting from discontinuities in the implicit function • It can give holes in the reconstruction

29. Implicit surface methods • Hoppe et al.’s method, missing step: outward normal orientation: • make a Euclidean minimum spanning tree EMST on all points • find the topmost point in the EMST and make it the root of the tree • orient the root so that its normal is upwards (dot product with vertical upward direction is positive) • pre-order traverse the spanning tree and assign the normal of a node by letting it be consistent with its parent (their dot product should be positive)

31. root flip 1st flip 2nd flip 4th flip 5th flip 3rd

33. Implicit surface methods • Hoppe et al.’s method, missing step: outward normal orientation: • make a Euclidean minimum spanning tree on all points • Take all n choose 2 pairs of points and sort them by distance • Start with n points as sets with only one element • Loop over the point pairs by increasing distance: accept a pair as an edge in the EMST if the points are in different sets, then unite their sets (stop when there is only one set) • we can take only the pairs of points whose Voronoi cells are neighbors (typically fewer than quadratically many)

34. Implicit surface methods • Hoppe et al.’s method: outward normal orientation: • Problem: the method may make mistakes when sampling is low near areas with high curvature

35. Implicit surface methods • Hoppe et al.’s method: outward normal orientation: • Problem: the method may make mistakes when sampling is low near areas with high curvature • Solution: • First make a graph G where every point is connected to its k nearest neighbors (k is a small constant to be chosen) • Give every edge a weight depending on to what extent the normals are parallel (including inverted): 1 – [the absolute value of the dot product of the normalized normalsat these points] • Compute the minimum spanning tree of G using these weights [ normalized = scaled to have unit length ]

36. Implicit surface methods • All of this works in 3D too • Note that in 3D the desired spanning tree will not have such a simple path structure as you would expect in 2D

37. Implicit surface methods • More advanced: Let f be composed of the sum of many “local” implicit functions fi, one for each pi • These local implicit functions are blended into f • The weight wi of fiat a point x depends on the distance between x and pi; it decreases monotonically with the distance • The weights must make an unbiased interpolator/ estimator of the local implicit functions

38. Implicit surface methods • The functions fi could be chosen as in Hoppe et al.: the signed distance to the normal plane at pi • The weights wi can also be chosen to be a linear fall-off function, or a Gaussian fall-off function • If for any point x, f(x) isinfluenced by only few fi(with weight wi > 0), thenthe surface can be recon-structed efficiently • In Gaussian case, use cut-off wi distance

39. Voronoi and Delaunay methods • The Voronoi diagram and Delaunay triangulation of a set of points sampled on a surface have a lot of structure that can be used for reconstruction

40. Reconstructing with Delaunay • Possibilities: • Let the object be the union of Delaunay triangles (2D) or tetrahedra (3D) that are deemed to be inside the shape • Decide of edges (2D) or triangles (3D) that they are part of the boundary of the shape α-shape

41. Reconstructing with Voronoi • Possibilities: • Use the Voronoi diagram to estimate normals as the diameters of the Voronoi cells • Extract the medial axis from the Voronoi diagram of the points; it resembles the medial axis of the reconstruction

42. The α-shape • The α-shape is a general shape descriptor for a set of points [ Edelsbrunner, Kirkpatrick, Seidel 1983]

43. The α-shape • The α-shape is the straight-line graph with the α-extreme points as the vertices and the α-neighbors as the edges α-neighbors radius α-disk α-extreme point

44. The α-shape • Every α-shape edge is also a Delaunay triangulation edge (it has an empty circle through its endpoints) α-neighbors radius α-disk α-extreme point

45. The α-shape • The choice of αis important • bigger α: avoids holes and separate components in shape, but also less detail • smaller α: more detail but risk of holes and separate components • How to determine the right α? • Can we make α adaptive: smaller where point density is higher and larger where point density is lower?

46. α-shapes

47. α-shapes in 3D

48. α-shapes in 3D

49. The α-shape • We can compute the 2D α-shape from the Delaunay triangulation by testing every edge: determine the radius of the smallest and largest empty circle through its endpoints Circles containing p and q have their centers on the bisector of p and q p Empty circles containing p and q have their centers between the centers of the circumcircles of the two triangles incident to edge pq q

50. The α-shape p largest q p This center realizes the smallest circle This center realizes the smallest circle q largest