TY - JOUR
T1 - Metrics and methods for evaluating model-driven reality capture plans
AU - Ibrahim, Amir
AU - Golparvar-Fard, Mani
AU - El-Rayes, Khaled
N1 - The authors would like to acknowledge the financial support of National Science Foundation (NSF) Grants 1446765 and 1544999. The authors also appreciate the support of Reconstruct Inc. and all other construction companies who offered the real‐world project data. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They do not necessarily reflect the view of the NSF, industry partners, or professionals mentioned above.
The authors would like to acknowledge the financial support of National Science Foundation (NSF) Grants 1446765 and 1544999. The authors also appreciate the support of Reconstruct Inc. and all other construction companies who offered the real-world project data. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors. They do not necessarily reflect the view of the NSF, industry partners, or professionals mentioned?above.
PY - 2022/1
Y1 - 2022/1
N2 - This paper presents new metrics and methods for evaluating the quality of reality capture plans—commonly used to operate camera-mounted unmanned aerial vehicles (UAVs) or ground rovers—for construction progress monitoring and inspection of as-is conditions. Using 4D building information model (BIM) or 3D reality model as a priori, these metrics provide feedback on the quality of a plan (within a few minutes), accounting for resolution, visibility, accuracy, completeness of the capture, and satisfying battery capacity and line-of-sight requirements. A cloud-based system is introduced to create and optimize UAV/rover missions in the context of prior model. Results from real-world construction data sets demonstrate that the proposed metrics offer actionable insights into the accuracy and completeness of reality capture plans. Additionally, a capture plan—with a combination of canonical and noncanonical camera views—that satisfies the introduced metrics is statistically correlated with the quality of reconstructed reality. These metrics can improve computer-vision progress monitoring and inspection methods that rely on the construction site's appearance and geometry.
AB - This paper presents new metrics and methods for evaluating the quality of reality capture plans—commonly used to operate camera-mounted unmanned aerial vehicles (UAVs) or ground rovers—for construction progress monitoring and inspection of as-is conditions. Using 4D building information model (BIM) or 3D reality model as a priori, these metrics provide feedback on the quality of a plan (within a few minutes), accounting for resolution, visibility, accuracy, completeness of the capture, and satisfying battery capacity and line-of-sight requirements. A cloud-based system is introduced to create and optimize UAV/rover missions in the context of prior model. Results from real-world construction data sets demonstrate that the proposed metrics offer actionable insights into the accuracy and completeness of reality capture plans. Additionally, a capture plan—with a combination of canonical and noncanonical camera views—that satisfies the introduced metrics is statistically correlated with the quality of reconstructed reality. These metrics can improve computer-vision progress monitoring and inspection methods that rely on the construction site's appearance and geometry.
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U2 - 10.1111/mice.12693
DO - 10.1111/mice.12693
M3 - Article
AN - SCOPUS:85105119107
SN - 1093-9687
VL - 37
SP - 55
EP - 72
JO - Computer-Aided Civil and Infrastructure Engineering
JF - Computer-Aided Civil and Infrastructure Engineering
IS - 1
ER -