Classification of Sensor Independent Point Cloud Data of Building Objects using Random Forests

The Architectural, Engineering and Construction (AEC) industry is looking to integrate Building Information Modelling (BIM) for existing buildings. Currently these as-built models are created manually which is time-consuming. An important step in the automated Scan-to-BIM procedure is the interpretation and classification of point cloud data. This is computationally challenging due to the sheer size of point cloud data of an entire building. Additionally, the variety of objects makes classification problematic. Existing methods focus on specific sensors or environments to improve their results. The goal of this research is to provide a method that is sensor independent and labels entire buildings at once. This paper presents a method to automatically identify structural elements for the purposes of Scan-to-BIM. More specifically, a Random Forests classifier is employed for the classification of the floors, ceilings, roofs, walls and beams. First, the point cloud is pre-segmented into planar primitives. This significantly reduces the data while maintaining accuracy. Both contextual and geometric features are used to describe the observed patches.By presegmenting the data, more distinct features can be extracted from the input information. The algorithm is evaluated using realistic data of a wide variety of existing buildings including houses, school facilities, a factory, a castle and a church. The experiments prove that the proposed algorithm is capable of labelling structural elements with reported precisions of 85% and 87% recall in highly cluttered environments. In future work, the classified patches are processed by class-specific reconstruction algorithms to create BIM geometry.

Jaar van publicatie:2019

BOF-keylabel:ja

IOF-keylabel:ja

BOF-publication weight:6

CSS-citation score:2

Authors from:Higher Education

Toegankelijkheid:Open