Scan2BIM-NET: Deep learning method for segmentation of point clouds for scan-to-BIM

Yeritza Perez-Perez, Mani Golparvar-Fard, Khaled El-Rayes

Research output: Contribution to journalArticlepeer-review

Abstract

The architecture, engineering, and construction (AEC) industry perform thousands of scans each year. The majority of these point clouds are used for generating three-dimensional (3D) models - a process formally known as scan to building information modeling (Scan-to-BIM) - that represent the current conditions of a construction scene. Although point cloud data provide the scene's geometric information, its use presents several challenges that make the process of generating a 3D model from point cloud data time-consuming, labor-intensive, and error-prone. In order to address the mentioned challenges, this paper presents a new end-to-end deep learning method, named Scan2BIM-NET, for semantically segmenting the structural, architectural, and mechanical components present in point cloud data. It classifies beam, ceiling, column, floor, pipe, and wall elements using three main networks: two convolutional neural network (CNN) and one recurrent neural network (RNN). The method was trained and tested using 83 rooms from point cloud data representing real-world industrial and commercial buildings. The process returned an average accuracy of 86.13%, and the beam, ceiling, column, floor, pipe, and wall categories obtained an accuracy of 82.47%, 92.60%, 59.31%, 98.71%, 82.79%, and 84.46%, respectively. The experimental results showed that deep learning improves the accuracy of semantic segmentation of architectural, structural, and mechanical components. This new method has the potential of being a tool during the Scan-to-BIM process, especially for semantically segmenting underceiling areas where mechanical components are close to structural elements.

Original languageEnglish (US)
Article number04021107
JournalJournal of Construction Engineering and Management
Volume147
Issue number9
DOIs
StatePublished - Sep 1 2021
Externally publishedYes

Keywords

  • Deep learning
  • Geometric segmentation
  • Point cloud
  • San-to-BIM
  • Semantic segmentation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Industrial relations
  • Strategy and Management

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