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

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Literature Review 10 author attempts here to estimate, mostly qualitatively, such performance expectations. These are used in the rest of this thesis for assessing the performance of automated 3D object recognition systems within the investigated context. Accuracy: Accuracy refers to the performance of the system to correctly extract from a given scan all the as-built point clouds corresponding to project 3D objects, and to correctly assign each extracted range point to the right object. Such performance can be measured using fundamental recall rate, specificity rate, etc. While perfect recall rates could be expected, the recognition of the as-built point cloud of certain project 3D objects could also be considered more critical than of other ones. For instance, when considering a scan of a steel structure, it can be argued that it is more critical, both for dimensional quality control and progress tracking, to be able to correctly extract the as-built point clouds of all beams and columns than of panel braces. In the investigated context, it is thus difficult to quantitatively set targets for performance measures such as recall rate, and a qualitative analysis of object recognition results may be preferred. Nonetheless, these fundamental measures are used in this thesis with the goal of setting benchmarks that can be used for comparison with future research results. Robustness: Robustness refers to the performance of the system to correctly extract 3D objects’ point clouds in laser scans with different levels of clutter and, more critically, occlusions. This is very important since, as is shown in Figure 2.3, objects are often scanned with partial and sometimes significant occlusion. In the investigated context, an object recognition system should be able to recognize objects with high levels of occlusions. Note that occlusions can be categorized in two types: (1) internal occlusions are due due to other project 3D objects (e.g. columns and walls); and (2) external occlusions are due to non-project objects (e.g. equipment and temporarily stored materials). A good system should be robust with both types of occlusions. Efficiency: Efficiency refers to the speed of the 3D object recognition system. Such a system is intended to support many applications such as progress tracking and dimensional QA/QC that provide key information to design-making processes. Thus, having real-time project 3D status information would be preferable. However, as discussed previously, currently available systems for progress control provide information on a daily basis at best, and, as is reported by Navon and Sacks [87], construction managers do not (yet) seem to have a strong desire for information updates at a higher frequency than daily. Since the time needed to conduct a site laser scan is in the order of
Literature Review 11 minutes (at most one hour), it can be concluded that it would be appropriate if a system for extracting from a scan all the clouds corresponding to project 3D objects took no more than a few hours. Level of automation: Having a fully automated system is preferable since it would not be subject to human error and would probably be more efficient. However, as described in Section 2.2, current approaches for recording the 3D as-built information are manually intensive. Therefore, a system with some level of automation, and consequently less manually intensive than current approaches, while providing information at least as accurate would be an improvement. Figure 2.3: A typical construction laser scan of a scene with clutter, occlusions, similarly shaped objects, symmetrical objects, and non search objects. It should be emphasized that no approach based on 2D images to date comes close to these performance objectives [21, 22]. This is why the industry is adopting 3D imaging as a basis for field applications. This thesis is the first effort to extract object 3D status information automatically from construction range images and thus it will establish a benchmark for performance that subsequent work will improve on. In the next section, classic approaches for 3D object recognition from the robotics and machine vision literature are summarized though not extensively reviewed. 2.5 Automated 3D Object Recognition in Range Images The problem of automatically recognizing construction projects’ 3D objects in site sensed 3D data is a model-based 3D object recognition problem. Model-based 3D
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 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: Literature Review 9 Figure 2.2: Tot
Page 27 and 28: Literature Review 13 tigated here.
Page 29 and 30: Literature Review 15 the recognitio
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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
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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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