The utilization of machine-based recommendation has been leveraged in countless industries, from suggestive search on the web, to photo stock image recommendation. At its core, a recommendation engine can query relevant information –text, images, etc– among vast databases and surface it to the user, as he/she interacts with a given interface. As large 3D data warehouses are being aggregated today, Architecture & Design could benefit from similar practices.
In fact, the design process in our discipline happens mostly through the medium of 3D software (Rhinoceros 3D, Maya, 3DSmax, AutoCAD). Might it be through CAD software(Computer-Aided Design), or today BIM engines (Building Information Modeling), Architects constantly translate their intention into lines and surfaces in 3D space. Suggesting relevant 3D objects, taken from exterior data sources, could be a way to enhance their design process.
This is the goal of this article: study and propose a way to assist designers, through “suggestive modeling”. As architects draw in 3D space, an array of machine-learning-based classifiers would be able to search for relevant suggestions and propose alternative, similar or complementary design options.
To that end, taking inspiration from precedents in the field of 3D shape recognition & classification, we come up with a methodology and a toolset able to suggest models to designers as they draw. Our goal is, in fact, twofold: (1) to speed up 3D-modeling process with pre-modeled suggestions, while (2) inspiring designers through alternative or complementary design options.