Enhancing Architectural Image Processing: A Novel 2D to 3D Algorithm Using Improved Convolutional Neural Networks

Qianying Zou ; Fengyu Liu ; Yuan Liao

Computer Science and Information Systems, Tome 21 (2024) no. 4.

Résumé

In light of the escalating advancements in architectural intelligence and information technology, the construction of smart cities increasingly necessitates a higher degree of precision in architectural measurements. Conventional approaches to architectural measurement, characterized by their low efficiency and protracted execution time, need to be revised to meet these burgeoning demands. To address this gap, we introduce a novel architectural image processing model that synergistically integrates Restricted Boltzmann Machines (RBMs) with Convolutional Neural Networks (CNNs) to facilitate the conversion of 2D architectural images into 3D. In the implementation phase of the model, an initial preprocessing of the architectural images is performed, followed by depth map conversion via bilateral filtering. Subsequently, minor voids in the images are rectified through a neighborhood interpolation algorithm. Finally, the preprocessed 2D images are input into the integrated model of RBMs and CNNs, realizing the 2D to 3D conversion. Experimental outcomes substantiate that this novel model attains a precision rate of 97%, and significantly outperforms comparative algorithms in terms of both runtime and efficiency. These results compellingly corroborate our model’s superiority in architectural image processing, enhancing measurement accuracy and drastically reducing execution time.

Détail

Keywords: Building image; Boltzmann machine; Convolution neural network; Bilateral filtering; Neighborhood difference; 2D to 3D