Abstract
A new approach to visualize the laser-scanned point cloud of historical buildings is introduced. It has 2 advanced features: transparent rendering effect and an unsupervised extraction of architectural information. The transparent rendering is conducted using our previously reported rendering tool, Stochastic Point-based Rendering (SPBR). The architectural information extraction is realized with the point cloud clustering method, which considers pre-segmented sub point sets as the primitive units in the feature space for clustering instead of using raw points. This method increases the accuracy and reduces the computational cost of architectural information extraction.
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Acknowledgment
The authors would like to thank Rui Xu for the technical assistance. We also thank the cooperation of the history and folk culture museum of Ritto (Shiga, Japan). This work was supported by JSPS KAKENHI Grant Number 16H02826.
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Sheng, W., Hasegawa, K., Okamoto, A., Tanaka, S. (2016). Visualizing the Architectural Structure of a Historical Building by Clustering Its Laser-Scanned Point Cloud. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 645. Springer, Singapore. https://doi.org/10.1007/978-981-10-2669-0_1
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DOI: https://doi.org/10.1007/978-981-10-2669-0_1
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