Earticle

초록

영어

In recent years, the extensive use of BIM has brought a revolutionary change for the construction industry. As an important technology to support BIM, three-dimensional digital technology has become a hot academic research. The 3D geometric model is the main data expression of modes in BIM environment. But because of the complexity of 3D modeling, the maintenance of the 3D model library in BIM environment will spend a lot of time and cost. The traditional CAD 3D modeling has accumulated a large number of 3D models for the BIM project to reuse. Using the 3D model classification technology can quickly classify the existing 3D model, and save a lot of cost. Deep learning is in recent years a new method of machine learning. In this paper, we use Stacked Auto-Encoders (SAE) to classify 3D models under the environment of BIM. Experiments show that, the method proposed in the paper has achieved good results in the 3D model classification, which provides a new idea for the development of BIM.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Building Information Modeling (BIM)
  2.3. 3D Model Classification
  2.4. Deep Learning
 3. 3D Model Feature Descriptors
  3.1. Model Coordinates Standardization and Pretreatment
  3.2. Feature Extraction
 4. 3D Models Classification using SAE
  4.1. Stacked auto-Encoders Network (SAE)
  4.2. SAE Based 3D Model Classification
 5. Experiments
  5.1. Dataset
  5.2. Feature Description
  5.3. Experimental Results and Analysis
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• Li Wang [ School of Mechanics & Civil Engineering, China University of Mining and Technology, Xuzhou, China ]
• Zhikai Zhao [ IoT Perception Mine Research Center, China University of Mining and Technology, Xuzhou , China ]
• Xuefeng Wu [ Xuhai College, China University of Mining and Technology, Xuzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605