Spatial template-based geometric complexity reduction method for photo-realistic modeling of large-scale indoor spaces

Recent progresses in image-based rendering (IBR) have demonstrated the feasibility of photo-realistic modeling in room-scale indoor spaces. However, it is difficult to extend the method to large-scale indoor spaces, because the computational complexity increases exponentially as the geometric complexity increases. In this study, we propose a framework that automatically generates photo-realistic model of large-scale indoor spaces. We first define primary factors that increase geometrical complexity as geometrically excluded objects (GEOs). The proposed framework removes GEOs in images and point clouds to efficiently represent large-scale indoor spaces. To this end, we introduce a segmentation method to segment GEOs from every image coherently. In addition, we also introduce an image inpainting method to fill in the segmented images for photo-realistic indoor modeling. Experiments are conducted in three small-scale spaces and two large-scale indoor spaces. In the experiments, the proposed modules are validated thoroughly. In addition, the experimental results show that the proposed method enables to generate photo-realistic indoor models automatically and efficiently.