Learnable Geometry and Connectivity Modelling of BIM Objects

Abstract

Accurate modelling of object geometries and their connectivity is a critical yet often overlooked aspect of the 3D scan to Building Information Model (BIM) pipeline. It is essential for the extraction of high-level structural information of infrastructure. In this paper, we first propose a novel method for parametric modelling of both primitive and non-primitive geometries. Element models are generated from predictions using a differentiable method, enabling both the integration of fitting error into the loss function, as well as further optimisation of predictions using gradient descent. This eliminates the need for custom distance heuristics, allowing for scalability to any object with parametric geometry. We evaluate our method on a novel benchmark and demonstrate that it accurately predicts model parameters despite the presence of occlusions. Moreover, we validate the utility of the extracted parameters by adopting them to infer connectivity between objects in a scan. This is achieved by framing connectivity inference as a link prediction task on a Graph Neural Network (GNN). This method is the first to learn the underlying nature of connectivity relationships within a BIM model, and significantly outperforms current rule-based methods for connectivity inference. Furthermore, we release a new synthetic dataset of industrial facility BIM element scans.