Delaunay Triangulation: Applications Reconstruction Meshing 1 - - PowerPoint PPT Presentation

delaunay triangulation applications reconstruction meshing
SMART_READER_LITE
LIVE PREVIEW

Delaunay Triangulation: Applications Reconstruction Meshing 1 - - PowerPoint PPT Presentation

Delaunay Triangulation: Applications Reconstruction Meshing 1 Reconstruction From points 2 - 1 Reconstruction From points to shape 2 - 2 Reconstruction From points 2 - 3 Reconstruction From points to shape 2 - 4 Reconstruction


slide-1
SLIDE 1

1

Delaunay Triangulation: Applications Reconstruction Meshing

slide-2
SLIDE 2

2 - 1

Reconstruction

From points

slide-3
SLIDE 3

2 - 2

Reconstruction

From points to shape

slide-4
SLIDE 4

2 - 3

Reconstruction

From points

slide-5
SLIDE 5

2 - 4

Reconstruction

From points to shape

slide-6
SLIDE 6

2 - 5 From points

Reconstruction

slide-7
SLIDE 7

2 - 6 From points to shape

Reconstruction

slide-8
SLIDE 8

3

Reconstruction

Context Delaunay is a good start

(wanted result ⇢ Delaunay)

Crust 2D 0.4 sample ) wanted result ⇢ crust 0.25 sample ) crust ⇢ wanted result Algorithm 3D

slide-9
SLIDE 9

4 - 1

Reconstruction

Context Sensor Point set (no structure or unknown)

slide-10
SLIDE 10

4 - 2

Reconstruction

Context Medical Images

slide-11
SLIDE 11

4 - 3

Reconstruction

Context Medical Images

slide-12
SLIDE 12

4 - 4

Reconstruction

Context Childbirth simulation Surgery planning Radiotherapy planing Endoscopy simulation

slide-13
SLIDE 13

4 - 5

Reconstruction

Context Childbirth simulation Surgery planning Radiotherapy planing Endoscopy simulation

slide-14
SLIDE 14

4 - 6

Reconstruction

Context Sensor Point set (no structure or unknown) Scanner

slide-15
SLIDE 15

4 - 7

Reconstruction

Context Sensor Point set (no structure or unknown) Scanner Endoscope

slide-16
SLIDE 16

4 - 8

Reconstruction

Context Cultural heritage

slide-17
SLIDE 17

4 - 9

Reconstruction

Context Cultural heritage

slide-18
SLIDE 18

4 - 10

Reconstruction

Context

slide-19
SLIDE 19

4 - 11

Reconstruction

Context Reverse engineering

slide-20
SLIDE 20

4 - 12

Reconstruction

Context Reverse engineering Prototyping (3D print) Quality control

slide-21
SLIDE 21

4 - 13

Reconstruction

Context Sensor Point set (no structure or unknown)

slide-22
SLIDE 22

4 - 14

Reconstruction

Context Sensor Point set (no structure or unknown) Laser illuminate in a plane

slide-23
SLIDE 23

4 - 15

Reconstruction

Context Sensor Point set (no structure or unknown) Laser illuminate in a plane Camera

slide-24
SLIDE 24

4 - 16

Reconstruction

Context Sensor Point set (no structure or unknown) Laser illuminate in a plane Camera Image

slide-25
SLIDE 25

4 - 17

Reconstruction

Context Sensor Point set (no structure or unknown) Laser illuminate in a plane Camera Get 3D position

slide-26
SLIDE 26

4 - 18

Reconstruction

Context Geology

slide-27
SLIDE 27

4 - 19

Reconstruction

Context Sensor Point set (no structure or unknown) Geology

slide-28
SLIDE 28

4 - 20

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-29
SLIDE 29

4 - 21

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-30
SLIDE 30

4 - 22

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-31
SLIDE 31

4 - 23

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-32
SLIDE 32

4 - 24

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section Can be solve using Voronoi diagrams

slide-33
SLIDE 33

4 - 25

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-34
SLIDE 34

4 - 26

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-35
SLIDE 35

4 - 27

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-36
SLIDE 36

4 - 28

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-37
SLIDE 37

4 - 29

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-38
SLIDE 38

4 - 30

Reconstruction

Context Sensor Point set (no structure or unknown) Abstract 3D problem that we can solve in 2D section

slide-39
SLIDE 39

5 - 1

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-40
SLIDE 40

5 - 2

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-41
SLIDE 41

5 - 3

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-42
SLIDE 42

5 - 4

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-43
SLIDE 43

5 - 5

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-44
SLIDE 44

5 - 6

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-45
SLIDE 45

5 - 7

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-46
SLIDE 46

5 - 8

Reconstruction

Delaunay is a good start Medial axis of a curve (surface in 3D) Locus of center of bitangent spheres

slide-47
SLIDE 47

6 - 1

Reconstruction

Delaunay is a good start ✏-sample of a curve Local feature size:

slide-48
SLIDE 48

6 - 2

Reconstruction

Delaunay is a good start ✏-sample of a curve Local feature size: x lfs(x) =

slide-49
SLIDE 49

6 - 3

Reconstruction

Delaunay is a good start ✏-sample of a curve Local feature size: x lfs(x) = distance(x, medial axis) lfs(x)

slide-50
SLIDE 50

6 - 4

Reconstruction

Delaunay is a good start ✏-sample of a curve Local feature size: Sample is an

slide-51
SLIDE 51

6 - 5

Reconstruction

Delaunay is a good start ✏-sample of a curve Local feature size: x lfs(x) = distance(x, medial axis) lfs(x) Sample is an if 8x, Disk(x, ✏·lfs(x))\Sample6= ;

slide-52
SLIDE 52

7 - 1

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma:

slide-53
SLIDE 53

7 - 2

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma:

slide-54
SLIDE 54

7 - 3

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma:

slide-55
SLIDE 55

7 - 4

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma: Disk\Curve has 2 cc A and B A B c

slide-56
SLIDE 56

7 - 5

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma: Disk\Curve has 2 cc A and B A B a = closest of c on Curve(wlog on A) b = closest of c on B b a c

slide-57
SLIDE 57

7 - 6

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma: Disk\Curve has 2 cc A and B A B a = closest of c on Curve(wlog on A) b = closest of c on B b a Moving from c to a dist to B % c

slide-58
SLIDE 58

7 - 7

Reconstruction

Delaunay is a good start 8 Disk, Disk\Curve has a single connected component

  • r Disk\Medial axis6= ;

Lemma: Disk\Curve has 2 cc A and B A B a = closest of c on Curve(wlog on A) b = closest of c on B b a Moving from c to a dist to B % reach center of bitangent disk a0 c

slide-59
SLIDE 59

8 - 1 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem x0

slide-60
SLIDE 60

8 - 2 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem disks centered on Curve, through x x0 Two neighboring points along curve

slide-61
SLIDE 61

8 - 3 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem disks centered on Curve, through x x0 Two neighboring points along curve

slide-62
SLIDE 62

8 - 4 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem disks centered on Curve, through x x0 Two neighboring points along curve

slide-63
SLIDE 63

8 - 5 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem x0 xx0 neighbors on curve ) no points on cc xx0 in

slide-64
SLIDE 64

8 - 6 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem 1-sampling ) ⇢ x0 xx0 neighbors on curve ) no points on cc xx0 in

slide-65
SLIDE 65

8 - 7 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem 1-sampling ) ⇢ x0 xx0 neighbors on curve ) no points on cc xx0 in Lemma }) no other cc \

slide-66
SLIDE 66

8 - 8 x

Reconstruction

Delaunay is a good start If Sample is a ✏-sample, ✏ < 1 neighboring points along Curve are Delaunay neighbors Theorem 1-sampling ) ⇢ x0 xx0 neighbors on curve ) no points on cc xx0 in Lemma }) no other cc \ ) empty

slide-67
SLIDE 67

9 - 1

Reconstruction

Delaunay is a good start Given a sampling

slide-68
SLIDE 68

9 - 2

Reconstruction

Delaunay is a good start Given a sampling Compute Delaunay

slide-69
SLIDE 69

9 - 3

Reconstruction

Delaunay is a good start Given a sampling Compute Delaunay Search the good sequence of edges there

slide-70
SLIDE 70

10 - 1

Reconstruction

Delaunay is a good start 1-sample is not enough

slide-71
SLIDE 71

10 - 2

Reconstruction

Delaunay is a good start 1-sample is not enough

slide-72
SLIDE 72

10 - 3

Reconstruction

Delaunay is a good start 1-sample is not enough

slide-73
SLIDE 73

10 - 4

Reconstruction

Delaunay is a good start 1-sample is not enough

slide-74
SLIDE 74

11 - 1

Reconstruction

Crust 2D Algorithm

slide-75
SLIDE 75

11 - 2

Reconstruction

Crust 2D Algorithm Compute Voronoi diagram

slide-76
SLIDE 76

11 - 3

Reconstruction

Crust 2D Algorithm Keep Voronoi vertices

slide-77
SLIDE 77

11 - 4

Reconstruction

Crust 2D Algorithm Keep Voronoi vertices Compute Delaunay triangulation

slide-78
SLIDE 78

11 - 5

Reconstruction

Crust 2D Algorithm Keep Voronoi vertices Compute Delaunay triangulation Keep edges between original points

slide-79
SLIDE 79

11 - 6

Reconstruction

Crust 2D Algorithm Keep edges between original points

slide-80
SLIDE 80

12 - 1

Reconstruction

Crust 2D Algorithm

slide-81
SLIDE 81

12 - 2

Reconstruction

Crust 2D Algorithm

slide-82
SLIDE 82

12 - 3

Reconstruction

Crust 2D Algorithm

slide-83
SLIDE 83

12 - 4

Reconstruction

Crust 2D Algorithm

slide-84
SLIDE 84

12 - 5

Reconstruction

Crust 2D Algorithm

slide-85
SLIDE 85

12 - 6

Reconstruction

Crust 2D Algorithm

slide-86
SLIDE 86

12 - 7

Reconstruction

Crust 2D Algorithm

slide-87
SLIDE 87

12 - 8

Reconstruction

Crust 2D Algorithm

slide-88
SLIDE 88

13 - 1

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem:

slide-89
SLIDE 89

13 - 2

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve

slide-90
SLIDE 90

13 - 3

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v

slide-91
SLIDE 91

13 - 4

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma)

slide-92
SLIDE 92

13 - 5

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

slide-93
SLIDE 93

13 - 6

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  lfs

slide-94
SLIDE 94

13 - 7 tangent disk is empty

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  lfs

slide-95
SLIDE 95

13 - 8

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  lfs ✏ wlog lfs=1 and r  ✏

slide-96
SLIDE 96

13 - 9

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  lfs ✏ 1 r wlog lfs=1 and r  ✏

slide-97
SLIDE 97

13 - 10

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓ 

↵ 2

r r = 2 sin ↵

2

slide-98
SLIDE 98

13 - 11

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓ 

↵ 2

x0 x

  • = ⇡ ⇡↵

2

1 r r r = 2 sin ↵

2

slide-99
SLIDE 99

13 - 12

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

 ⇡

2 + arcsin r 2

✓ 

↵ 2

x0 x

  • = ⇡ ⇡↵

2

r r = 2 sin ↵

2

slide-100
SLIDE 100

13 - 13

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  ⇡

2 + arcsin r 2

 +

slide-101
SLIDE 101

13 - 14

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  ⇡

2 + arcsin r 2

 +  r + 2r sin ⇡

4 + 1 2arcsin r 2

slide-102
SLIDE 102

13 - 15

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  ⇡

2 + arcsin r 2

 +  r + 2r sin ⇡

4 + 1 2arcsin r 2

  • lfs= 1 contradiction is reached

if

slide-103
SLIDE 103

13 - 16

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem: x x0 x, x0 two neighboring points on Curve Curve Circle thru x and x0 centered on Curve By contradiction assume v 2 v intersects another cc of curve

(by Lemma) ✓

R  2r sin ✓

2

✓  ⇡

2 + arcsin r 2

 +  r + 2r sin ⇡

4 + 1 2arcsin r 2

  • lfs= 1 contradiction is reached

if

r + 2r sin ⇡

4 + 1 2arcsin r 2

  • Plot
slide-104
SLIDE 104

13 - 17

Reconstruction

Crust 2D

0.4 sample ) wanted result ⇢ crust

0.4 sample ) wanted result ⇢ crust

Theorem:

slide-105
SLIDE 105

14 - 1

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

slide-106
SLIDE 106

14 - 2

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0

slide-107
SLIDE 107

14 - 3

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle

slide-108
SLIDE 108

14 - 4

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle No Voronoi vertices there = )

slide-109
SLIDE 109

14 - 5

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle No Voronoi vertices there = ) No sample points there

slide-110
SLIDE 110

14 - 6

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle

slide-111
SLIDE 111

14 - 7

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

 ⇡

2 + 2arcsin r 2 ↵ 4

= ⇡ ⇡↵

2

r ' 2 sin ↵

4

r

slide-112
SLIDE 112

14 - 8

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

 ⇡

2 + 2arcsin r 2 ↵ 4

= ⇡ ⇡↵

2

r ' 2 sin ↵

4

r

slide-113
SLIDE 113

14 - 9

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle ⇡

4 2 arcsin r 2

biggest of two angles

slide-114
SLIDE 114

14 - 10

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle ⇡

4 2 arcsin r 2

biggest of two angles 2 y kxyk 2 sin

slide-115
SLIDE 115

14 - 11

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle ⇡

4 2 arcsin r 2

biggest of two angles 2 y kxyk 2 sin By Lemma, circle xx0yintersects medial axis

slide-116
SLIDE 116

14 - 12

Reconstruction

Crust 2D

0.25 sample ) crust ⇢ wanted result

Theorem:

0.25 sample ) crust ⇢ wanted result

x x0 Assume empty circle ⇡

4 2 arcsin r 2

biggest of two angles 2 y kxyk 2 sin Compute ✏ to ensure that

1 ✏ ⇥

encloses

slide-117
SLIDE 117

15 - 1

Reconstruction

3D

slide-118
SLIDE 118

15 - 2

Reconstruction

3D Difficulty: sliver

slide-119
SLIDE 119

15 - 3

Reconstruction

3D Difficulty: sliver small sphere

slide-120
SLIDE 120

15 - 4

Reconstruction

3D Difficulty: sliver small sphere four sample points

slide-121
SLIDE 121

15 - 5

Reconstruction

3D Difficulty: sliver small sphere four sample points almost flat Delaunay tetrahedron

slide-122
SLIDE 122

15 - 6

Reconstruction

3D Difficulty: sliver small sphere four sample points almost flat Delaunay tetrahedron Which triangle belongs to reconstruction ?

slide-123
SLIDE 123

15 - 7

Reconstruction

3D Difficulty: sliver small sphere four sample points almost flat Delaunay tetrahedron Which triangle belongs to reconstruction ? Crust: Voronoi vertices may kill useful triangles

slide-124
SLIDE 124

16 - 1

Reconstruction

3D

slide-125
SLIDE 125

16 - 2

Reconstruction

3D

slide-126
SLIDE 126

16 - 3

Reconstruction

3D

slide-127
SLIDE 127

16 - 4

Reconstruction

3D Pole = farthest of seed

slide-128
SLIDE 128

16 - 5

Reconstruction

3D Pole = farthest of seed 2nd pole= farthest of 1st pole

slide-129
SLIDE 129

16 - 6

Reconstruction

3D Pole = farthest of seed 2nd pole= farthest of 1st pole Approximate normal Approximate medial axis ! crust

slide-130
SLIDE 130

16 - 7

Reconstruction

3D Pole = farthest of seed 2nd pole= farthest of 1st pole Approximate normal Approximate medial axis ! crust Do not kill slivers

slide-131
SLIDE 131

41

T h e e n d