Abstract
AbstractStyle transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format.
Highlights
Convolutional Neural Networks (CNN) is a class of deep neural networks that are most commonly used to analyze visual images
The input image is fed through the CNN and the network activation is sampled at each convolutional layer of the VGG-19 architecture [6]
2D geometry is successfully converted into a 3D format that is available for architectural design
Summary
Convolutional Neural Networks (CNN) is a class of deep neural networks that are most commonly used to analyze visual images. The computer reads the image as pixels and represents it as a matrix, which will be processed by the convolutional layer. This layer uses a set of learnable filters that convolve across the width and height of the input file and compute the dot product to give the activation map. The layer is the fully connected layer, in which the neurons are fully connected to all activation of the previous layer Their activation can be calculated by matrix multiplication followed by offset. CNN uses relatively less preprocessing than other image classification algorithms This means that the network learns manually designed filters in traditional algorithms. This independence from prior knowledge and human effort in feature design is a major advantage
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