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Improve Computer Visualization of Architecture Based on the Bayesian Network

by Tao Shen1, Yukari Nagai1, Chan Gao2

Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi City, Ishikawa 923-1292, Japan.
Architecture Department, Huzhou University, Huzhou, Zhejiang, 313000, China.

* Corresponding Author: Tao Shen. Email: email.

Computers, Materials & Continua 2019, 58(2), 307-318. https://doi.org/10.32604/cmc.2019.04876

Abstract

Computer visualization has marvelous effects when it is applied in various fields, especially in architectural design. As an emerging force in the innovation industry, architects and design agencies have already demonstrated the value of architectural visual products in actual application projects. Based on the digital image technology, virtual presentation of future scenes simulates architecture design, architectural renderings and multimedia videos. Therefore, it can help design agencies transform the theoretical design concept into a lively and realistic visual which can provide the audience with a clearer understanding of the engineering and construction projects. However, it is challenging for designers to produce satisfactory renderings due to the frequent fault data during rendering. In this paper, we use the 3Ds MAX as the operating platform and we present an algorithm based on the Bayesian network to construct a vector representation of the fault data. On this basis, a case study of 3D Max’ application has also been presented.

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APA Style
Shen, T., Nagai, Y., Gao, C. (2019). Improve computer visualization of architecture based on the bayesian network. Computers, Materials & Continua, 58(2), 307-318. https://doi.org/10.32604/cmc.2019.04876
Vancouver Style
Shen T, Nagai Y, Gao C. Improve computer visualization of architecture based on the bayesian network. Comput Mater Contin. 2019;58(2):307-318 https://doi.org/10.32604/cmc.2019.04876
IEEE Style
T. Shen, Y. Nagai, and C. Gao, “Improve Computer Visualization of Architecture Based on the Bayesian Network,” Comput. Mater. Contin., vol. 58, no. 2, pp. 307-318, 2019. https://doi.org/10.32604/cmc.2019.04876

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cc Copyright © 2019 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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