Synthetic data generation using building information models

Infrastructure scene understanding from image data aids diverse applications in construction and maintenance. Recently, deep learning models have been employed to extract information regarding infrastructure from visual data. The performance of these models depends significantly on the volume of training data. However, preparing the training data is time-consuming and laborious, as it entails labeling numerous images. To address this issue, this paper proposes a method for generating high-quality synthetic data that includes the automatic annotation of infrastructure scenes. The method consists of three steps: 1) translating building information model (BIM) images into real-world images, 2) automatically labeling them using the spatial information contained in the BIM to generate various synthetic datasets, and 3) splicing the selected synthetic datasets together to form the final synthetic dataset. The Mask R-CNN models trained with building and bridge synthetic data achieved average precisions of 71.6% and 84.9%, respectively.