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29 March 2019 Direct structural element recognition from scattered point cloud data (Conference Presentation)
Tsung-Chin Hou, Yu-Min Su, Cheng-Yan Wu
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Proceedings Volume 10973, Smart Structures and NDE for Energy Systems and Industry 4.0; 109730J (2019) https://doi.org/10.1117/12.2515416
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States
Abstract
This study attempted to build up an automated post-processing for structural element reconstruction/recognition directly from the scattered point cloud data (PCD). The target structure specimen being scanned was the three-story RC frame constructed in NCREE South Laboratory before shacking table test was performed. Algorithms used for the element reconstruction/recognition including edge signature extraction and data clustering. Edges of the target structure were extracted directly from the raw PCD and the elements were clustered by DBSCAN to ensure the geometric appropriateness. Recognition rate and dimension accuracy were compared with blue print to quantify the recognition quality, accordingly. Study results shows that the automated post-processing can achieve 100% of recognition rate with 95% of dimension accuracy, suggesting that PCD is suitable for computer vision in recognizing structure elements.
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Tsung-Chin Hou, Yu-Min Su, and Cheng-Yan Wu "Direct structural element recognition from scattered point cloud data (Conference Presentation)", Proc. SPIE 10973, Smart Structures and NDE for Energy Systems and Industry 4.0, 109730J (29 March 2019); https://doi.org/10.1117/12.2515416
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KEYWORDS
Clouds
Computer vision technology
Detection and tracking algorithms
Machine vision
Reconstruction algorithms
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