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

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Contents 1 Introduction 1 1.1 Background And Motivation . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.5 Structure of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Literature Review 5 2.1 Performance-driven Projects and Control Processes in the AEC&FM industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Feedback 3D Information Flows . . . . . . . . . . . . . . . . . . . . 6 2.3 Leveraging New Reality-Capture Sensors . . . . . . . . . . . . . . . 7 2.3.1 Laser Scanning . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.4 Performance Expectations for 3D Object Recognition in Construction 9 2.5 Automated 3D Object Recognition in Range Images . . . . . . . . . 11 2.6 A Priori Information Available in the AEC&FM Context . . . . . . 15 2.6.1 3D CAD Modeling . . . . . . . . . . . . . . . . . . . . . . . 16 2.6.2 3D Registration . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.7 Conclusion on Using Existing 3D Object Recognition Techniques . . 19 3 New Approach 21 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Step 1 - Project 3D Model Format Conversion . . . . . . . . . . . . 22 3.3 Step 2 - 3D Registration . . . . . . . . . . . . . . . . . . . . . . . . 24 vi
3.4 Step 3 - Calculation of the As-planned Range Point Cloud . . . . . 26 3.4.1 The Ray Shooting Problem . . . . . . . . . . . . . . . . . . 27 3.4.2 Developed Approach . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Step 4 - Range Points Recognition . . . . . . . . . . . . . . . . . . . 34 3.5.1 Automated Estimation of Δρmax . . . . . . . . . . . . . . . 35 3.6 Step 5 - Objects Recognition . . . . . . . . . . . . . . . . . . . . . . 38 3.6.1 Sort Points . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.6.2 Recognize Objects . . . . . . . . . . . . . . . . . . . . . . . 39 3.6.3 Automated Estimation of Surfmin . . . . . . . . . . . . . . 41 3.7 Sensitivity Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4 Experimental Analysis of the Approach’s Performance 45 4.1 Level of Automation . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.2 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.3 Accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.3.1 Accuracy Performance Metrics . . . . . . . . . . . . . . . . . 48 4.3.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . 49 4.3.3 Automated Estimation of Δρmax . . . . . . . . . . . . . . . 53 4.3.4 Automated Estimation of Surfmin . . . . . . . . . . . . . . 54 4.4 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 4.4.1 Planned Internal Occlusion Rate . . . . . . . . . . . . . . . 58 4.4.2 Total Occlusion Rate (TOR) . . . . . . . . . . . . . . . . . . 59 4.5 Efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.5.1 Overall Computational Performance . . . . . . . . . . . . . . 61 4.5.2 Performance of The Developed Technique for Calculating Asplanned Range Point Clouds . . . . . . . . . . . . . . . . . . 62 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 5 Enabled Applications: Feasibility Analysis and Experiments 67 5.1 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 5.2 APPC: Automated Construction Progress Tracking . . . . . . . . . 68 vii
Page 1 and 2: Automated Recognition of 3D CAD Mod
Page 3 and 4: Abstract There is shift in the Arch
Page 5: Dedication This thesis is dedicated
Page 9 and 10: D Construction of the Bounding Volu
Page 11 and 12: 5.4 Progress recognition results fo
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
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New Approach 43 Furthermore, the ca
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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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