FaçAID: A Transformer Model for Neuro-Symbolic Facade Reconstruction

Document Type

Conference Proceeding

Publication Date

12-3-2024

Abstract

We introduce a neuro-symbolic transformer-based model that converts flat, segmented facade structures into procedural definitions using a custom-designed split grammar. To facilitate this, we first develop a semi-complex split grammar tailored for architectural facades and then generate a dataset comprising of facades alongside their corresponding procedural representations. This dataset is used to train our transformer model to convert segmented, flat facades into the procedural language of our grammar. During inference, the model applies this learned transformation to new facade segmentations, providing a procedural representation that users can adjust to generate varied facade designs. This method not only automates the conversion of static facade images into dynamic, editable procedural formats but also enhances the design flexibility, allowing for easy modifications.

Identifier

85217211102 (Scopus)

ISBN

[9798400711312]

Publication Title

Proceedings - SIGGRAPH Asia 2024 Conference Papers, SA 2024

External Full Text Location

https://doi.org/10.1145/3680528.3687657

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