Semantic-Vertex-Based Topological Detection for Automatic Dimension Generation in Building Information Modeling (BIM) with Industry Foundation Classes (IFC)

In this study, a topological matching algorithm is introduced for semantic vertex detection to automate dimension generation in a building information modeling (BIM) environment based on the Industry Foundation Classes (IFC) standard. Conventional IFC-based quantity take-off (QTO) methods provide only standardized attributes, such as height, length, width, and area; therefore, user-defined custom dimensions—such as net opening sizes or parameter lengths—must be calculated manually. This study proposes a method for fully automating the dimensions required by users by automatically tagging and visualizing semantic vertices for geometrically identical IFC objects. These semantic vertices correspond to representative topological feature points (e.g., left–bottom–origin, left–top–front, left–bottom–back, and right–bottom–front). Based on these defined semantic vertices, the method automatically establishes vertex correspondence among objects to generate dimensions. The proposed workflow comprises four main stages: (1) geometry normalization of IFC objects, (2) semantic vertex definition, (3) automatic detection of semantic vertices, and (4) dimension generation and visualization. The experimental results demonstrate that the proposed approach successfully enables the computation of dimensions for geometrically identical objects, thereby significantly improving the efficiency of QTO processes.