Towards automatic generation of as-built BIM: 3D building facade modeling and material recognition from images

Abstract

As-built building information model (BIM) is an urgent need of the architecture, engineering, construction and facilities management (AEC/FM) community. However, its creation procedure is still labor-intensive and far from maturity. Taking advantage of prevalence of digital cameras and the development of advanced computer vision technology, the paper proposes to reconstruct a building facade and recognize its surface materials from images taken from various points of view. These can serve as initial steps towards automatic generation of as-built BIM. Specifically, 3D point clouds are generated from multiple images using structure from motion method and then segmented into planar components, which are further recognized as different structural components through knowledge based reasoning. Windows are detected through a multilayered complementary strategy by combining detection results from every semantic layer. A novel machine learning based 3D material recognition strategy is presented. Binary classifiers are trained through support vector machines. Material type at a given 3D location is predicted by all its corresponding 2D feature points. Experimental results from three existing buildings validate the proposed system.