Digital tutors renderman manual#

This technique allows the option to colorize and convert the segmented images into a predicted image based on colors and simple geometry. The overall process has been done using Google Collaboratory as the computational platform and a Pix2Pix notebook this notebook demonstrates image to image translation using conditional GAN’s (1).

The initial dataset used to train the machine consists of 300 images for each segmentation process (total 600 images) as expected, results vary based on the architecture of the ML model and the input data.

Digital tutors renderman manual#

The segmentation has been done using two different methods: 1- Segmentation using K-Means clustering within Grasshopper (as per 1st iteration below) and 2- creating a manual dataset to improve accuracy for the predictions (as per 2nd and 3rd iteration). This case study evolves with the initial idea of using different segmentation processes for real-time visualizations to generate an initial data set used to train the machine and provide initial testing within this field. For now, design goals have been identified and created by architects/designers however, artificial intelligence and machine-learning algorithms can improve the visualization of multiple unseen options to optimize and inform initial visualizations. The evolution of GAN’’s and the use of ANN within the architectural paradigm opens the opportunity to explore and recreate existing rendering datasets created for multiple projects allowing the creation of a new tool able to predict and generate conceptual imagery for early design phases. Visualizations and rendering processes have become a significant component of the overall design workflow for existing and new projects however, this task is considered one of the most time-consuming within the overall workflow. Due to the creation of new digital technologies and the relation of computation within architecture and design, designers face new opportunities to investigate and incorporate new tools that inform better design solutions. This case study intends to use machine learning to generate a tool that can help reduce time visualizing new designs the intention is to integrate machine learning for conceptual rendering and open the opportunity to further investigate in this field. The rapid development of urban centers and the evolution of architecture, machine learning, artificial intelligence and generative design have brought the opportunity to rethink traditional design workflows and provide a broader design vision.