VOX2BIM+ - A Fast and Robust Approach for Automated Indoor Point Cloud Segmentation and Building Model Generation

Abstract

Building Information Modeling (BIM) plays a key role in digital design and construction and promises also great potential for facility management. In practice, however, for existing buildings there are often either no digital models or existing planning data is not up-to-date enough for use as as-is models in operation. While reality-capturing methods like laser scanning have become more affordable and fast in recent years, the digital reconstruction of existing buildings from 3D point cloud data is still characterized by much manual work, thus giving partially or fully automated reconstruction methods a key role. This article presents a combination of methods that subdivide point clouds into separate building storeys and rooms, while additionally generating a BIM representation of the building’s wall geometries for use in CAFM applications. The implemented storeys-wise segmentation relies on planar cuts, with candidate planes estimated from a voxelized point cloud representation before refining them using the underlying point data. Similarly, the presented room segmentation uses morphological operators on the voxelized point cloud to extract room boundaries. Unlike the aforementioned spatial segmentation methods, the presented parametric reconstruction step estimates volumetric walls. Reconstructed objects and spatial relations are modelled BIM-ready as IFC in one final step. The presented methods use voxel grids to provide relatively high speed and refine their results by using the original point cloud data for increased accuracy. Robustness has proven to be rather high, with occlusions, noise and point density variations being well-tolerated, meaning that each method can be applied to data acquired with a variety of capturing methods. All approaches work on unordered point clouds, with no additional data being required. In combination, these methods comprise a complete workflow with each singular component suitable for use in numerous scenarios.

Indoor Clutter Object Removal Method for an As-Built Building Information Model Using a Two-Dimensional Projection Approach

“…They utilized the horizontal slice between the floor and ceiling, which is the most clutter-free slice. Martens and Blankenbach [26] adopted morphological operations on the x-y plane of a point cloud to remove indoor noise. Wu et al [27] developed a Modified Ring-Stepping Clustering (M-RSC) method to extract structural element lines in complex indoor environments.…”

Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

“…Other investigations have explored alternative techniques from image processing to automatically segment and generate BIM models from point cloud data. Martens et al [29] introduced the VOX2BIM method, which converts point clouds into voxel representations and employs 2D representations and morphological operations for BIM model generation. While this approach offers computational advantages, it risks data loss through conversion, and their research primarily focused on indoor environments.…”

Hierarchical SVM for Semantic Segmentation of 3D Point Clouds for Infrastructure Scenes

“…A notable scientific investigation explored different approaches for segmenting and generating BIM models from point cloud data. The study introduced the VOX2BIM method, which involved converting point clouds into voxel representations and employing diverse techniques to automate the process of BIM model generation [13]. Other research investigations have explored the automation of the workflow for point cloud processing, which is an essential step in the Scan2Bim process [14].…”