Modeling of Architectural Components for Large-Scale Indoor Spaces From Point Cloud Measurements

Abstract

In this letter, we propose a method to model architectural components in large-scale indoor spaces from point cloud measurements. The proposed method enables the modeling of curved surfaces, cylindrical pillars, and slanted surfaces, which cannot be modeled using existing approaches. It operates by constructing the architectural points from the raw point cloud after removing non-architectural (objects) points and filling in the holes caused by their exclusion. Then, the architectural points are represented using a set of piece-wise planar segments. Finally, the adjacency graph of the planar segments is constructed to verify the fact that every planar segment is closed. This ensures a watertight mesh model generation. Experimentation using 14 different real-world indoor space datasets and 2 public datasets, comprising spaces of various sizes-from room-scale to large-scale (12,557 m$^{2}$), verify the accuracy of the proposed method in modeling environments with curved surfaces, cylindrical pillars, and slanted surfaces.