- Page 1 and 2: To the Graduate Council:I am submit
- Page 3 and 4: Copyright c○ Yiyong Sun, 2002All
- Page 5 and 6: AcknowledgmentsFirst and foremost,
- Page 7 and 8: search employs an implicit surface-
- Page 9 and 10: 3.2.2 Edge Preservation for Range D
- Page 11 and 12: 8.2 Future Research . . . .........
- Page 13 and 14: List of Figures1.1 A framework for
- Page 15 and 16: 7.14 Finding corresponding points o
- Page 17 and 18: Chapter 1IntroductionThis introduct
- Page 19 and 20: Range ImageSmoothingRegistrationCol
- Page 21 and 22: obust to surface noise and registra
- Page 23 and 24: more efficient than 2D image correl
- Page 25 and 26: scending path, and region merging.L
- Page 27 and 28: two-dimensional (2D) image, 2D lowp
- Page 29: curvatures of points on a surface i
- Page 33 and 34: with different surface representati
- Page 35 and 36: center point and every surface poin
- Page 37 and 38: Table 2.1: Comparison of different
- Page 39 and 40: Reconstruction from Unorganized Poi
- Page 41 and 42: tional. An offset of the distance f
- Page 43 and 44: surement confidence. However, in or
- Page 45 and 46: triangle mesh. The triangle mesh is
- Page 47 and 48: mesh segmentation based on the phys
- Page 49 and 50: Rettmann et al. [70] proposed a mes
- Page 51 and 52: successful smoothing for both raw r
- Page 53 and 54: where lim t→0 (R/t) =0,H represen
- Page 55 and 56: the range r ij is estimated instead
- Page 57 and 58: y minimizing the following energy f
- Page 59 and 60: tions were used:r α = r i,j+1 −
- Page 61 and 62: where κ represents a parameter tha
- Page 63 and 64: inequality, such asI∑I∑S i ≤
- Page 65 and 66: n ′ in iT ipqn ipw in ′ i−→
- Page 67: The singular value decomposition is
- Page 70 and 71: a limit on sharp edges. This limit
- Page 72 and 73: of the smoothing.Surface smoothing
- Page 74 and 75: fingerprint are presented. Section
- Page 76 and 77: erroneously includes surface patch
- Page 78 and 79: (ρ, θ)ρθT p (S)expSexp p (ρ,
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exponential map is introduced.4.2 F
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accurately than Dijkstra’s algori
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andθ = arccos ρ2 (A)+|AB| 2 −
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andy =((n × −→ pm) × n) · v
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images.4.3 Candidate Point Selectio
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(a)(b)(c)(d)Figure 4.6: Global fing
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and R ij is below a threshold. The
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If the singular-value decomposition
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Chapter 5Surface Reconstruction fro
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Figure 5.2: Two registered and over
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Figure 5.4: Intersecting triangles.
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Figure 5.7: Gap between surfaces af
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5.2 Volume-Based Surface Integratio
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2D texture coordinate of x is set t
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ABFigure 5.9: Hole filling illustra
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are presented in Section 7.4.6.1 A
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6.3 Applying the Fast Marching Wate
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Algorithm 6.2 (Fast Marching Waters
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15: end if16: if v i.h
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6.3.2 Geodesic ErosionThis step ass
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(a)(b)(c)(d)Figure 6.3: Segmenting
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Chapter 7Experimental ResultsThis c
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no discernible improvement. For fai
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(a)(b)(c)(d)Figure 7.4: Surface smo
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(a)(b)(c)(d)Figure 7.6: Smoothing s
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(a)(b)(c)(d)(e)(f)Figure 7.8: Adapt
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The real range images were scanned
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(a)(b)Figure 7.10: Registration of
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(a)(b)(c)(d)(e)(f)Figure 7.12: Matc
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(a) (b) (c) (d)(e) (f) (g) (h)(i)(j
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(a)(b)ICP Registration Error (mm)21
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(a)(b)(c)(d)(e)(f)Figure 7.17: Surf
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Table 7.1: Small parts 3D reconstru
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(a)(b)(c)(d)Figure 7.21: Surface mo
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(a)(b)Figure 7.23: Automatic hole f
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(a)(b)(c)(d)(e)(f)(g)(h)Figure 7.25
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(a)(b)(c)(d)Figure 7.27: Four-view
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(a)(b)(c)(d)Figure 7.28: Segmentati
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(a)(b)(c)(d)Figure 7.30: Surface se
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Table 7.2: Performance of the fast
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surface reconstructed from the raw
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to choose a proper flowing step siz
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of interest based on the shape irre
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8.2 Future ResearchThere are many o
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Bibliography[1] E. L. Allgower and
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[26] G. Dziuk and J. E. Hutchinson.
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[54] W. E. Lorensen and H. E. Cline
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[81] M. Soucy and D. Laurendeau. A
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[106] S. M. Yamany, A. A. Farag, an
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Appendix ALaser Range ScannersLaser
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(a)(b)Transmitter LensTargetDiodeLa
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A.2 Scanners Based on Laser Triangu
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LaserBaseline B-s0saOpticalCenter(a
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Appendix BSimplification of theArea
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VitaYiyong Sun was born in Beijing,