PhD Thesis - Automated Recognition of 3D CAD Model Objects in ...

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List of Tables 4.1 Level of automation and frequency of each of the five steps constituting the developed object recognition approach. . . . . . . . . . . 46 4.2 Characteristics of the Trimble TM GX 3D scanner. . . . . . . . . . . 47 4.3 Day, Number, ID, number of scanned points, resolution and mean registration error (ɛReg with respect to the 3D model) for the five scans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.4 Number of visually identified objects in the investigated five scans. . 49 4.5 Automatically estimated Δρmax and Surfmin thresholds for the five experiments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 4.6 Object recognition results for the five scans (the values in third and fourth columns are numbers of objects). . . . . . . . . . . . . . . . 51 4.7 Object recognition results for the five scans (the values in third and fourth columns are as-planned (expected) covered surfaces in m 2 ). . 52 4.8 Computational times (in seconds) of the steps 2 to 5 of the recognition process for the five scans. . . . . . . . . . . . . . . . . . . . . . 61 4.9 Comparison of the computational performances of the developed object recognition technique using a MSABV-based BVH (Experiment 5 ) and of the common technique using a sphere-based BVH (Experiment 5’). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.1 Recognition results for the day d1 (values in columns 3 and 4 are numbers of objects). . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5.2 Recognition results for the day d2 (values in columns 3 and 4 are numbers of objects). . . . . . . . . . . . . . . . . . . . . . . . . . . 70 5.3 Progress recognition results for the periods d0–d1 and d1–d2 where recognized progress is calculated using the method in Section Progress Calculation Method (values in columns 3 and 4 are numbers of objects). 71 x
5.4 Progress recognition results for the periods d0–d1 and d1–d2 using the relaxed method for the calculation of the recognized progress (values in columns 3 and 4 are numbers of objects). . . . . . . . . . . . . . 72 5.5 Planned vs. scanned objects for the five scans (the values in third and fourth columns are numbers of objects). . . . . . . . . . . . . . 79 5.6 Planned vs. scanned objects for the five scans (the values in third and fourth columns are the sums of objects’ as-planned covered surfaces in m 2 ). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 G.1 Recognition Statistics for Scan 3. . . . . . . . . . . . . . . . . . . . 131 H.1 Mathematical notations. . . . . . . . . . . . . . . . . . . . . . . . . 142 H.2 Notations of the variables used in the algorithms. . . . . . . . . . . 143 xi
Page 1 and 2: Automated Recognition of 3D CAD Mod
Page 3 and 4: Abstract There is shift in the Arch
Page 5 and 6: Dedication This thesis is dedicated
Page 7 and 8: 3.4 Step 3 - Calculation of the As-
Page 9: D Construction of the Bounding Volu
Page 13 and 14: 5.2 Example of a model and recogniz
Page 15 and 16: Chapter 1 Introduction 1.1 Backgrou
Page 17 and 18: Introduction 3 Sub-objectives are f
Page 19 and 20: Chapter 2 Literature Review This ch
Page 21 and 22: Literature Review 7 is also often i
Page 23 and 24: Literature Review 9 Figure 2.2: Tot
Page 25 and 26: Literature Review 11 minutes (at mo
Page 27 and 28: Literature Review 13 tigated here.
Page 29 and 30: Literature Review 15 the recognitio
Page 31 and 32: Literature Review 17 recognition of
Page 33 and 34: Literature Review 19 frame of the e
Page 35 and 36: Chapter 3 New Approach 3.1 Overview
Page 37 and 38: New Approach 23 Data: Model, Scan R
Page 39 and 40: New Approach 25 frame. Therefore, f
Page 41 and 42: New Approach 27 The complexity of t
Page 43 and 44: New Approach 29 are tested for inte
Page 45 and 46: New Approach 31 effectiveness of a
Page 47 and 48: New Approach 33 (a) 3D View. (b) To
Page 49 and 50: New Approach 35 Data: PB, BVH Resul
Page 51 and 52: New Approach 37 α, of each as-plan
Page 53 and 54: New Approach 39 Data: Scan,Model Re
Page 55 and 56: New Approach 41 where: α is the as
Page 57 and 58: New Approach 43 Furthermore, the ca
Page 59 and 60: Chapter 4 Experimental Analysis of
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Experimental Analysis of the Approa
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Chapter 5 Enabled Applications: Fea
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Enabled Applications: Feasibility A
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Chapter 6 Conclusions and Recommend
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Conclusions and Recommendations 83
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Appendix A Description of the STL F
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Appendix A 87 solid name facet norm
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Appendix B 89 Figure B.1: Spherical
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Appendix B 91 Data: [x, y, z] Resul
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Appendix C 93 (a) 3D View. (b) Top
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Appendix C 95 C.1.1 Bounding Pan An
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Appendix C 97 Figure C.5: Illustrat
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Appendix C 99 Data: ϕmintemp, ϕmi
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Appendix C 101 Figure C.6: Example
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Appendix C 103 Data: {Facet} Result
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Appendix D Construction of the Boun
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Appendix D 107 Figure D.1: Illustra
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Appendix D 109 (a) MSABV not inters
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Appendix D 111 It can be noted that
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Appendix E Containment of a Ray in
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Appendix E 115 Finally, in the case
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Appendix F Calculation of the Range
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Appendix F 119 second plane perpend
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Appendix F 121 included in Algorith
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Appendix G 123 Figure G.1: 3D CAD m
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Appendix G 125 G.2.2 Step 2 - 3D mo
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Appendix G 127 G.2.4 Step 4 - Recog
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Appendix G 129 It can be seen in Fi
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Appendix G 131 Column 7: Whether th
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Appendix G 133 Table G.1 - continue
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Appendix H 143 Table H.1 - continue
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Appendix H 145 Table H.2 - continue
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Appendix H 147 [11] J. Amanatides.
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Appendix H 149 [35] G. S. Coleman a
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Appendix H 151 [57] B. Hofmann-Well
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Appendix H 153 [81] A. S. Mian, M.
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Appendix H 155 [104] L. M. Sheppard
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PhD Thesis - Automated Recognition of 3D CAD Model Objects in ...

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