Development of Deep Learning-Based Automatic Classification of Architectural Objects in Point Clouds for BIM Application in Renovating Aging Buildings

Tae-Hoon Kim; Hyeong-Mo Gu; Soon-Min Hong; Seoung-Yeon Choo

doi:10.13161/kibim.2023.13.4.096

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2023 Vol.13, Issue 4 Preview Page Next Page

Research Article

Development of Deep Learning-Based Automatic Classification of Architectural Objects in Point Clouds for BIM Application in Renovating Aging Buildings 딥러닝 기반 노후 건축물 리모델링 시 BIM 적용을 위한 포인트 클라우드의 건축 객체 자동 분류 기술 개발

31 December 2023. pp. 96-105

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Abstract

This study focuses on developing a building object recognition technology for efficient use in the remodeling of buildings constructed without drawings. In the era of the 4th industrial revolution, smart technologies are being developed. This research contributes to the architectural field by introducing a deep learning-based method for automatic object classification and recognition, utilizing point cloud data. We use a TD3D network with voxels, optimizing its performance through adjustments in voxel size and number of blocks. This technology enables the classification of building objects such as walls, floors, and roofs from 3D scanning data, labeling them in polygonal forms to minimize boundary ambiguities. However, challenges in object boundary classifications were observed. The model facilitates the automatic classification of non-building objects, thereby reducing manual effort in data matching processes. It also distinguishes between elements to be demolished or retained during remodeling. The study minimized data set loss space by labeling using the extremities of the x, y, and z coordinates. The research aims to enhance the efficiency of building object classification and improve the quality of architectural plans by reducing manpower and time during remodeling. The study aligns with its goal of developing an efficient classification technology. Future work can extend to creating classified objects using parametric tools with polygon-labeled datasets, offering meaningful numerical analysis for remodeling processes. Continued research in this direction is anticipated to significantly advance the efficiency of building remodeling techniques.

Keywords

Remodeling

Point Cloud

Semantic Segmentation

Automatic Classification

Object Generation

키워드

리모델링

포인트 클라우드

시멘틱 세그멘테이션

자동 분류

객체 생성

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Information

Publisher :Journal of KIBIM
Publisher(Ko) :한국BIM학회
Journal Title :Journal of KIBIM
Journal Title(Ko) :한국BIM학회논문집
Volume : 13
No :4
Pages :96-105
Received Date : 2023-11-02; 2023-12-19
Accepted Date : 2023-12-19
DOI :https://doi.org/10.13161/kibim.2023.13.4.096

Journal of KIBIM ISSN:2288-1697(Print) 2288-1697(Online) 한국BIM학회논문집

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