To the Graduate Council: I am submitting herewith a dissertation ...

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through. For example, a real world 3D mapping looks more realistic than a syntheticscene in a driving simulator. In reverse engineering, 3D reconstruction is able to generatea CAD model from a real object. By using 3D FAX, a digitized model can be transferredto another location where a new model is quickly replicated. In image guided surgery, 3Dmodels reconstructed from CTs and MRIs can help a doctor see the patient’s anatomicstructure, and 3D analysis such as segmentation will help make an accurate diagnosis.3D models are reconstructed from a set of measurements in 3D space. Laser rangesensing and stereo vision are two popular methods for 3D measurement. Although stereovision devices are much cheaper than laser range scanners, they are limited by measurementaccuracy and range. Stereo vision relies on finding the corresponding points ontwo spatially separated images and using triangulation to get the 3D measurement. Thisprocess is sensitive to illumination, and it is difficult to get dense measurements. Therequirement of a large base line for long range measurements is impractical in manycases. This research uses laser range scanners to digitize the 3D space. Scanners basedon time-of-flight are used to capture long range 3D information.Scanners based onlaser triangulations are used to measure small objects.The motivation of this research is to build a framework for surface modeling andanalysis using the range data captured by range scanners.This dissertation makescontributions to solving several difficult problems in building such a framework.2
Range ImageSmoothingRegistrationColor ImageIntegrationRange Image3D ModelColor ImageSegmentationSimplificationFigure 1.1: A framework for 3D reconstruction and analysis.1.2 A Framework for 3D Reconstruction and AnalysisSeveral steps are involved in reconstructing a 3D model and further scene analysis usingrange images. The framework used in this research is illustrated in Fig. 1.1. After a setof range images are acquired by the range scanners, they are preprocessed to eliminatethe sampling noise. In the second step, the range images are registered into a globalcoordinate system. Then the registered surfaces are integrated into a whole 3D model.When color images are available, they are also integrated to get a textured model. In3D scene analysis, the model is segmented into different meaningful parts. Each step isessential for a complete framework of surface modeling and analysis. The framework,proposed in [108], segments the point cloud before performing integration. However,segmenting a surface is more robust than segmenting a cloud of points because a surfacecarries more geometric information. In the following text, a more detailed descriptionof these steps and expected challenges is presented.A range scanner acquires the distance between itself and an object. However, the3
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: Chapter 1IntroductionThis introduct
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 and 30: curvatures of points on a surface i
Page 31 and 32: smoothing on the surface mesh. Shri
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
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a limit on sharp edges. This limit
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of the smoothing.Surface smoothing
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fingerprint are presented. Section
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erroneously includes surface patch
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(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
Page 178 and 179:
Appendix ALaser Range ScannersLaser
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(a)(b)Transmitter LensTargetDiodeLa
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A.2 Scanners Based on Laser Triangu
Page 184 and 185:
LaserBaseline B-s0saOpticalCenter(a
Page 186 and 187:
Appendix BSimplification of theArea
Page 188:
VitaYiyong Sun was born in Beijing,
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