Synthetic 3D Data Generation Pipeline for Geometric Deep Learning in Architecture

TL;DR

This paper presents a customizable synthetic data generation pipeline for architectural 3D models, facilitating deep learning tasks with diverse, annotated datasets tailored to specific research needs.

Contribution

The authors developed a modular, extendable framework for generating large, class-balanced 3D architectural datasets with customizable annotations and views, supporting geometric deep learning.

Findings

Generated datasets enable effective training for geometric deep learning.

Framework allows extensive customization for various architectural research tasks.

All code and data are publicly available for community use.

Abstract

With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. We decided to tackle this problem by constructing a field-specific synthetic data generation pipeline that generates an arbitrary amount of 3D data along with the associated 2D and 3D annotations. The variety of annotations, the flexibility to customize the generated building and dataset parameters make this framework suitable for multiple deep learning tasks, including geometric deep learning that requires direct 3D supervision. Creating our building data generation pipeline we leveraged architectural knowledge from experts in order to construct a framework that would be modular, extendable and would provide a sufficient amount of class-balanced data samples. Moreover, we purposefully involve the researcher in the dataset customization allowing the introduction of additional building components, material textures, building classes, number and type of annotations as well as the number of views per 3D model sample. In this way, the framework would satisfy different research requirements and would be adaptable to a large variety of tasks. All code and data are made publicly available.