Abstract
Abstract. In this paper, we present an improved approach of enriching photogrammetric point clouds with semantic information extracted from images to enable a later automation of BIM modelling. Based on the DeepLabv3+ architecture, we use Semantic Segmentation of images to extract building components and objects of interiors. During the photogrammetric reconstruction, we project the segmented categories into the point cloud. Any interpolations that occur during this process are corrected automatically and we achieve a mIoU of 51.9 % in the classified point cloud. Based on the semantic information, we align the point cloud, correct the scale and extract further information. Our investigation confirms that utilizing photogrammetry and Deep Learning to generate a semantically enriched point cloud of interiors achieves good results. The combined extraction of geometric and semantic information yields a high potential for automated BIM model reconstruction.
Highlights
The digitalisation of the building sector is progressing steadily and, with Building Information Modeling (BIM), is taking the step from two-dimensional plans on paper to comprehensive, three-dimensional digital building models
Images are the main component of our approach because of the massive amount of information contained in them. They are the input for the photogrammetric point cloud generation and the extraction of objects based on Deep Learning methods
We were able to verify that the combination of photogrammetry and Deep Learning is a solid approach to generate a semantically enriched point cloud of interiors
Summary
The digitalisation of the building sector is progressing steadily and, with Building Information Modeling (BIM), is taking the step from two-dimensional plans on paper to comprehensive, three-dimensional digital building models. Developing an automatic extraction of the necessary information out of measurement data yields a high potential at simplifying the creation of such “As-Build” or “AsIs” models and the possibility to make them widely available. We present our approach to providing both semantic and geometric information of an interior room in a classified point cloud in an automated process
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