geometry clustering of shapes based on trained features
The goal is to get a set of geometries and cluster them to see patterns in geometric shapes.
As a prototype, we developed a sample set of 16 elements that were categorized into cones, cylinders, spheres, and boxes.
This set was trained in scikit-learn and Accord.NET through dodo, lunchbox, owl. Owl fit our needs in predictive modeling most effectively.
Shape grammar has been notoriously difficult to quantify over the years manually, so machine learning is a great application of this
Consistent Geometry Analysis
First, we take a dodecahedron to reduce polar biases and perform raycasting to determine vertex distances.
We also create a bounding box and then slice the building into upper, middle, and low slices as well as into levels describing terraces.
Additionally, we develop a contour and create a mesh to consistently calculate volume and area of the geometry.
The general properties we have focused on:
- surface area
- ratios of properties
To categorize the building shapes, we used a clustering algorithm that would automatically group buildings based on their geometric properties.
For clustering algorithms, we initially tried a regression classification to test plugins, but decided that Owl was the best solution for our use case. We used the following features:
- Aspect Ratios
- Slice Area
- Surface to Volume Ratio
- Vertex Mean Distance
- Vertex Farthest Distance
- Volume to Bounding Box Ratio
These provided us with enough features to distinguish differences between surrounding buildings.
We hope to include further feature analysis for our machine learning algorithms.