Intelligent pattern design using 3D modelling technology for urban sculpture designing

Abstract

3D modeling is actuality hired more and more by cities to improve urban planning and cultural protection. Sculptures in settlements are the main goal of this investigate into a novel 3D-Sculpture Architecture Estimation (3D-SAE) model. This model exploits Generative Adversarial Networks (GANs) to improve images, CNNs to extract features, and LDDNNHGS-ROA, a Novel Lightweight Deep Neural Network mutual with the Hunger Games Search and Remora Optimization Method, to categorize images. The GAN-based image development module reestablishes incapacitated or low-resolution sculpture photos, and the pre-trained CNN usages transfer learning to retrieve thorough features. The LDNN, tuned via HGS and ROA, brands sculpture image classification together effective and precise. This innovative method not only improves the precision of 3D reconstruction, but it also proposals a general tool for art conservationists, urban planners, and the general public in sympathetic and taking in urban sculptures. Participating these cutting-edge tools delivers a solid basis for investigating and interpreting public art, which potentials to improve cultural asset management, art conservation, and urban planning.

Keywords

• 3D modelling, Urban sculpture designing
• 3D-SAE (3D-Sculpture Analysis and Estimation)
• generative adversarial networks (GANs)
• Convolutional neural networks (CNNs)
• Novel Lightweight Deep Neural Network integrated with Hunger Games Search and Remora Optimization Algorithm (LDNN-HGS-ROA)