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http://dx.doi.org/10.7780/kjrs.2021.37.6.1.4

Evaluation of Clustered Building Solid Model Automatic Generation Technique and Model Editing Function Based on Point Cloud Data  

Kim, Han-gyeol (Image Engineering Research Center, 3DLabs Co., Ltd.)
Lim, Pyung-Chae (Image Engineering Research Center, 3DLabs Co., Ltd.)
Hwang, Yunhyuk (Image Engineering Research Center, 3DLabs Co., Ltd.)
Kim, Dong Ha (Dept of Computer Science, Namseoul University)
Kim, Taejung (Department of Geoinformatic Engineering, Inha University)
Rhee, Sooahm (Image Engineering Research Center, 3DLabs Co., Ltd.)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_1, 2021 , pp. 1527-1543 More about this Journal
Abstract
In this paper, we explore the applicability and utility of a technology that generating clustered solid building models based on point cloud automatically by applying it to various data. In order to improve the quality of the model of insufficient quality due to the limitations of the automatic building modeling technology, we develop the building shape modification and texture correction technology and confirmed the resultsthrough experiments. In order to explore the applicability of automatic building model generation technology, we experimented using point cloud and LiDAR (Light Detection and Ranging) data generated based on UAV, and applied building shape modification and texture correction technology to the automatically generated building model. Then, experiments were performed to improve the quality of the model. Through this, the applicability of the point cloud data-based automatic clustered solid building model generation technology and the effectiveness of the model quality improvement technology were confirmed. Compared to the existing building modeling technology, our technology greatly reduces costs such as manpower and time and is expected to have strengths in the management of modeling results.
Keywords
Point cloud; Building model; Model Editing;
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Times Cited By KSCI : 4  (Citation Analysis)
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