An evaluation of pose-normalization algorithms for point clouds introducing a novel histogram-based approach

Building Information Modeling is growing more relevant as digital models are not only used during the construction phase but also throughout the building's life cycle. The digital representation of geometric, physical and functional properties enables new methods for planning, execution and operation. Digital models of existing buildings are commonly derived from surveying data such as laser scanning which needs to be processed either manually or automatically throughout various steps. Aligning point clouds along the coordinate system's main axes (also commonly known as pose normalization) is a task benefitting any point cloud processing workflow, be it manual or automated. With the goal of automating this task, we compare various existing methods and present our own approach based on point density histograms. We conclude this paper by comparing and discussing all methods in terms of speed and robustness.