# FaccAID: A Transformer Model for Neuro-Symbolic Facade Reconstruction

arXiv: https://arxiv.org/abs/2406.01829 · HTML: https://mlsanity.com/paper/2406.01829/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 63 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [DeepCAD: A Deep Generative Network for Computer-Aided Design Models](https://mlsanity.com/paper/2105.09492/) (2021)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [Scallop: A Language for Neurosymbolic Programming](https://mlsanity.com/paper/2304.04812/) (2023)
- [StructureNet: Hierarchical Graph Networks for 3D Shape Generation](https://mlsanity.com/paper/1908.00575/) (2019)
- [Generative Layout Modeling using Constraint Graphs](https://mlsanity.com/paper/2011.13417/) (2020)
- [BuildingNet: Learning to Label 3D Buildings](https://mlsanity.com/paper/2110.04955/) (2021)
- [Neurosymbolic Models for Computer Graphics](https://mlsanity.com/paper/2304.10320/) (2023)
- [LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data](https://mlsanity.com/paper/2202.00185/) (2022)
- [ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis](https://mlsanity.com/paper/2009.08026/) (2020)
- [MatFormer: A Generative Model for Procedural Materials](https://mlsanity.com/paper/2207.01044/) (2022)
- [Inverse Procedural Modeling of Facade Layouts](https://mlsanity.com/paper/1308.0419/) (2013)
- [City-GAN: Learning architectural styles using a custom Conditional GAN architecture](https://mlsanity.com/paper/1907.05280/) (2019)
- [Sketch-n-Sketch: Output-Directed Programming for SVG](https://mlsanity.com/paper/1907.10699/) (2019)

## Built on by (most cited first, 5 of 5)

- [Pro-DG: Procedural Diffusion Guidance for Architectural Facade Generation](https://mlsanity.com/paper/2504.01571/) (2025)
- [ProcGen3D: Learning Neural Procedural Graph Representations for Image-to-3D Reconstruction](https://mlsanity.com/paper/2511.07142/) (2025)
- [HeatMat: Simulation of City Material Impact on Urban Heat Island Effect](https://mlsanity.com/paper/2601.22796/) (2026)
- [What a Comfortable World: Ergonomic Principles Guided Apartment Layout Generation](https://mlsanity.com/paper/2604.08411/) (2026)
- [Beyond Segmentation: Structurally Informed Facade Parsing from Imperfect Images](https://mlsanity.com/paper/2604.09260/) (2026)

## Abstract (arXiv)

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.