Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds
Development of semantically rich as-built building information models (BIMs) presents an ongoing challenge for the global BIM and computing engineering communities. A plethora of approaches have been developed that, however, possess several common weaknesses: (1) heavy reliance on laborious manual or semiautomatic segmentation of raw data [e.g., two-dimensional (2D) images or three-dimensional (3D) point clouds]; (2) unsatisfactory results for complex scenes (e.g., furniture or nonstandard indoor settings); and (3) failure to use existing resources for modeling and semantic enrichment. This paper aims to advance a novel, derivative-free optimization (DFO)–based approach that can automatically generate semantically rich as-built BIMs of complex scenes from 3D point clouds. In layman’s terms, the proposed approach recognizes candidate BIM components from 3D point clouds, reassembles the components into a BIM, and registers them with semantic information from credible sources. The approach was prototyped in Autodesk Revit and tested on a noisy point cloud of office furniture scanned via a Google Tango smartphone. The results revealed that the semantically rich as-built BIM was automatically and correctly generated with a root-mean-square error (RMSE) of 3.87 cm in 6.44 s, which outperformed the well-known iterative closest point (ICP) algorithm. The approach was then scaled up to a large auditorium scene consisting of 293 chairs to generate a satisfactory output BIM with a precision of 81.9% and a recall of 80.5%. The semantic registration approach also proved superior to existing segmentation approaches in that it is segmentation-free and capable of processing complex scenes and reusing known information. In addition to these methodological contributions, this approach, properly scaled up, will open new avenues for creation of building/city information models from inexpensive data sources and support profound value-added applications such as smart building or smart city developments.
From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds
Development of semantically rich as-built building information models (BIMs) presents an ongoing challenge for the global BIM and computing engineering communities. A plethora of approaches have been developed that, however, possess several common weaknesses: (1) heavy reliance on laborious manual or semiautomatic segmentation of raw data [e.g., two-dimensional (2D) images or three-dimensional (3D) point clouds]; (2) unsatisfactory results for complex scenes (e.g., furniture or nonstandard indoor settings); and (3) failure to use existing resources for modeling and semantic enrichment. This paper aims to advance a novel, derivative-free optimization (DFO)–based approach that can automatically generate semantically rich as-built BIMs of complex scenes from 3D point clouds. In layman’s terms, the proposed approach recognizes candidate BIM components from 3D point clouds, reassembles the components into a BIM, and registers them with semantic information from credible sources. The approach was prototyped in Autodesk Revit and tested on a noisy point cloud of office furniture scanned via a Google Tango smartphone. The results revealed that the semantically rich as-built BIM was automatically and correctly generated with a root-mean-square error (RMSE) of 3.87 cm in 6.44 s, which outperformed the well-known iterative closest point (ICP) algorithm. The approach was then scaled up to a large auditorium scene consisting of 293 chairs to generate a satisfactory output BIM with a precision of 81.9% and a recall of 80.5%. The semantic registration approach also proved superior to existing segmentation approaches in that it is segmentation-free and capable of processing complex scenes and reusing known information. In addition to these methodological contributions, this approach, properly scaled up, will open new avenues for creation of building/city information models from inexpensive data sources and support profound value-added applications such as smart building or smart city developments.
From Semantic Segmentation to Semantic Registration: Derivative-Free Optimization–Based Approach for Automatic Generation of Semantically Rich As-Built Building Information Models from 3D Point Clouds
Xue, Fan (Autor:in) / Lu, Weisheng (Autor:in) / Chen, Ke (Autor:in) / Zetkulic, Anna (Autor:in)
29.03.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Semantic-Rich 3D CAD Models for Built Environments from Point Clouds: An End-to-End Procedure
| British Library Conference Proceedings | 2017