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

摘要

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 3.87cm in 6.44s, 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.