Construction of the 3D Reconstruction System of Building Construction Scene Based on Deep Learning

Abstract

The increasing complexity and enormity of construction projects, as well as the fact that the actual operation of construction schedule management still mainly relies on traditional manual management methods, have led to low efficiency of construction schedule management and caused many construction projects to have cost overruns and legal disputes due to schedule delays. Existing 3D reconstruction algorithms often lead to significant voids, distortions, or blurred parts in the reconstructed 3D models, while the machine learning-based 3D reconstruction algorithms are often only to reconstruct simple separated objects and represent them as 3D boxes. A novel architecture of semisupervised 3D reconstruction algorithm is proposed. The algorithm iteratively improves the quality of the original 3D reconstruction model by training a generative adversarial network model to a converged state. Only the prior observed 2D images are required as weakly supervised samples, without any dependence on prior knowledge of the 3D structure shape or reference observations. Experimental results show that this algorithmic framework has significant advantages over the current state-of-the-art 3D reconstruction methods on the standard 3D reconstruction test set.