The paper investigates the integration of 3D laser scanning, point cloud processing, and HBIM methodologies for the revitalization and renovation of historical buildings. The research discusses how laser scanning technologies combined with BIM workflows can support the digital documentation, analysis, restoration, maintenance, and adaptive reuse of architectural heritage. Through the acquisition of high-precision point cloud data and the generation of HBIM models, the study proposes a workflow for managing architectural information, interdisciplinary collaboration, visualization, and renovation planning. The methodology also includes simulation analyses, digital archiving, and performance evaluation to improve communication, decision-making, and conservation processes in heritage renewal projects.
Preliminary Study on Architectural Skin Design Method Driven by Neural Style Transfer
This paper explores the application of neural style transfer as an AI-assisted method for architectural skin design, aiming to enhance formal diversity and support conceptual design processes. The research investigates how convolutional neural networks can extract and recombine content and style features from different visual sources to generate alternative façade design proposals.
The methodology is based on the neural style transfer approach introduced by Gatys et al., implemented using a pre-trained VGG-19 network. As illustrated in the workflow diagram (Fig. 2, p. 744), the process defines content and style loss functions to iteratively optimize an output image that combines structural features from a content image with stylistic attributes from a reference image. The study applies this method to multiple sets of architectural images, including traditional Chinese buildings, modernist architecture, and urban skylines, combined with stylistic references such as Notre Dame, landscape painting, and science-fiction imagery. Results demonstrate that style transfer can generate diverse and visually suggestive façade configurations, supporting architects in the early design phase by providing rapid exploratory variations. However, the generated outputs remain conceptual and require further interpretation and development, highlighting the role of AI as a tool for inspiration rather than a deterministic design system.