A fast end-to-end method for automatic interior progress evaluation using panoramic images

Abstract

Interior construction makes up a large portion of project budget and time and is more prone to schedule delays. Most research efforts on progress management focus on exterior environment, while few on interior construction. Although progress monitoring methods based on laser point clouds and computer vision are investigated before, the problems of costly acquisition and creation of point clouds and images are still open, which impede the study of progress evaluation, particularly in interior construction environments where clutters and occlusions are universal. This paper introduces a method based on 360° panoramic images and deep learning for fast end-to-end interior progress evaluation in room units. The method takes only one or two 360° panoramic images as input, estimates key corners, generates and registers room layouts, and semantically segments sparse point cloud. With the extracted corners and segmentation results, the progress states of interior trades can be evaluated. The experimental results show that the proposed method based on deep learning techniques achieves comparable performance against those on public data sets with 3D Intersection over Union (3D IoU) of 83.69% vs 84.23%, Corner Error (CE) of 0.4% vs 0.69%, and mean class Interaction over Union (mIoU) of 70.28% vs 53.5%. A case study of an interior decoration project of a hotel is adopted to demonstrate the feasibility and practical capabilities of the proposed method.