A Novel Method for Detecting 3D Steel Structures in As-Built Point Clouds

• Rogério Pinheiro de Souza PUC-Rio
• Jan Hurtado PUC-Rio
• Cesar A. Sierra-Franco PUC-Rio
• Romeu Oliveira PUC-Rio
• Alberto Raposo PUC-Rio

Resumo

Building Information Modeling (BIM) integrates engineering data with 3D geometry, spatial relationships, and other properties to support construction projects throughout their entire lifecycle. Researchers have been focusing on how to automatically generate or update these models from 3D point clouds, considering existing buildings. Most of these studies have focused on recognizing planar structures (such as floors and walls) or cylindrical ones (such as pipelines). Only a few recent works have addressed the detection of steel structural elements, due to their particular geometry. In these approaches, the point cloud of each structural element was manually separated from the point cloud of the entire building. This practice poses a challenge, as manual segmentation of point clouds is a timeconsuming and subjective process. In this paper, we propose a new approach to automatically detect structural steel elements in 3D point clouds of existing buildings projects, without the need for prior segmentation and regardless of orientation. The proposed technique combines geometry processing algorithms with a machine learning strategy. The performance results demonstrate the effectiveness of our approach in detecting the desired elements. These results open up new possibilities for developing automated pipelines aimed at generating highly accurate 3D BIM models.

Palavras-chave: Point cloud compression, Geometry, Solid modeling, Three-dimensional displays, Pipelines, Buildings, Machine learning, Robustness, Steel, Noise level