Civil and Environmental Engineering

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This is the collection for the University of Waterloo's Department of Civil and Environmental Engineering.

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Browsing Civil and Environmental Engineering by Subject "3D reconstruction"

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    Automated Pipe Spool Recognition in Cluttered Point Clouds
    (University of Waterloo, 2016-05-06) Czerniawski, Thomas
    Construction management is inextricably linked to the awareness and control of 3D geometry. Progress tracking, quality assurance/quality control, and the location, movement, and assembly of materials are all critical processes that rely on the ability to monitor 3D geometry. Therefore, advanced capabilities in site metrology and computer vision will be the foundation for the next generation of assessment tools that empower project leaders, planners, and workers. 3D imaging devices enable the capture of the existing geometric conditions of a construction site or a fabricated mechanical or structural assembly objectively, accurately, quickly, and with greater detail and continuity than any manual measurement methods. Within the construction literature, these devices have been applied in systems that compare as-built scans to 3D CAD design files in order to inspect the geometrical compliance of a fabricated assembly to contractually stipulated dtolerances. However, before comparisons of this type can be made, the particular object of interest needs to be isolated from background objects and clutter captured by the indiscriminate 3D imaging device. Thus far, object of interest extraction from cluttered construction data has remained a manual process. This thesis explores the process of automated information extraction in order to improve the availability of information about 3D geometries on construction projects and improve the execution of component inspection, and progress tracking. Specifically, the scope of the research is limited to automatically recognizing and isolating pipe spools from their cluttered point cloud scans. Two approaches are developed and evaluated. The contributions of the work are as follows: (1) A number of challenges involved in applying RANdom SAmple Consensus (RANSAC) to pipe spool recognition are identified. (2) An effective spatial search and pipe spool extraction algorithm based on local data level curvature estimation, density-based clustering, and bag-of-features matching is presented. The algorithm is validated on two case studies and is shown to successfully extract pipe spools from cluttered point clouds and successfully differentiate between the specific pipe spool of interest and other similar pipe spools in the same search space. Finally, (3) the accuracy of curvature estimation using data collected by low-cost range-cameras is tested and the viability of use of low-cost range-cameras for object search, localization, and extraction is critically assessed.
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    A Contour Grouping Algorithm for 3D Reconstruction of Biological Cells
    (University of Waterloo, 2009-08-20T17:53:14Z) Leung, Tony Kin Shun
    Advances in computational modelling offer unprecedented potential for obtaining insights into the mechanics of cell-cell interactions. With the aid of such models, cell-level phenomena such as cell sorting and tissue self-organization are now being understood in terms of forces generated by specific sub-cellular structural components. Three-dimensional systems can behave differently from two-dimensional ones and since models cannot be validated without corresponding data, it is crucial to build accurate three-dimensional models of real cell aggregates. The lack of automated methods to determine which cell outlines in successive images of a confocal stack or time-lapse image set belong to the same cell is an important unsolved problem in the reconstruction process. This thesis addresses this problem through a contour grouping algorithm (CGA) designed to lead to unsupervised three-dimensional reconstructions of biological cells. The CGA associates contours obtained from fluorescently-labeled cell membranes in individual confocal slices using concepts from the fields of machine learning and combinatorics. The feature extraction step results in a set of association metrics. The algorithm then uses a probabilistic grouping step and a greedy-cost optimization step to produce grouped sets of contours. Groupings are representative of imaged cells and are manually evaluated for accuracy. The CGA presented here is able to produce accuracies greater than 96% when properly tuned. Parameter studies show that the algorithm is robust. That is, acceptable results are obtained under moderately varied probabilistic constraints and reasonable cost weightings. Image properties – such as slicing distance, image quality – affect the results. Sources of error are identified and enhancements based on fuzzy-logic and other optimization methods are considered. The successful grouping of cell contours, as realized here, is an important step toward the development of realistic, three-dimensional, cell-based finite element models.