Makine öğrenmesinin tasarım alanında kullanımı: grafik tabanlı GAN’lardan kütle modeli oluşturma

Year 2025, Volume: 8 Issue: 2, 621 - 639, 28.10.2025

Mustafa Koç , İmdat As

https://doi.org/10.37246/grid.1591809

Abstract

Bu makale, mimari grafik gösterimlerini üç boyutlu (3B) kavramsal kütle modellerine dönüştüren bir makine öğrenimi (ML) sistemi sunmaktadır. Öncelikle 2B kat planı oluşturmaya odaklanan önceki ML yaklaşımlarının aksine, yöntemimiz birden fazla bileşeni tek bir iş akışına entegre eder: (1) HouseGAN++ kullanarak grafik tabanlı girdi, (2) özel MATLAB görüntü işleme metotları ile görüntü tabanlı şekil çıkarma, (3) FloorplanToBlender ile 3B model oluşturma ve (4) görsel geliştirme için difüzyon model tabanlı stil transferi. Bu uçtan uca yaklaşım, otomatik plan-hacim dönüşümü ve üretken görüntü sentezi yoluyla estetik keşfin birleşiminde farklılık göstermektedir. Sonuçlar, geliştirilen sistemin manuel çabayı önemli ölçüde azaltırken verimli, çok aşamalı mimari fikir oluşturmayı sağladığını göstermektedir. Önerilen yöntem, kavram gelişimini hızlandırarak ve soyut mekânsal girdilerden stilistik olarak çeşitli çıktılar sunarak erken aşama tasarım süreçlerine katkıda bulunur.

Keywords

makine öğrenmesi , kütle modelleri , GAN , konsept tasarım , Grafik-tabanlı Derin Öğrenme

Ethical Statement

Araştırma etik standartlara uygun olarak yapılmıştır.

Supporting Institution

Bu araştırmanın yürütülmesi ve/veya makalenin hazırlanması için herhangi bir mali destek alınmamıştır

A Case Study of a Machine Learning Usage in Design: Conceptual Models from Graph-based GANs

Abstract

This paper presents a novel machine learning (ML) pipeline that transforms architectural graph representations into fully rendered three-dimensional (3D) conceptual massing models. Unlike previous ML approaches that focus primarily on 2D floorplan generation, our method integrates multiple components into a single workflow: (1) graph-based input using HouseGAN++, (2) image-based shape extraction via custom MATLAB processing, (3) 3D model construction with FloorplanToBlender, and (4) diffusion model–based style transfer for visual enhancement. This end-to-end approach is distinctive in its combination of automated plan-to-volume conversion and aesthetic exploration through generative image synthesis. The results show that our pipeline enables efficient, multi-stage architectural ideation while significantly reducing manual effort. The proposed method contributes to early-stage design processes by accelerating concept development and offering stylistically diverse outputs from abstract spatial inputs.

Keywords

machine learning , graph-based deep learning , conceptual designs , massing models , GAN

Ethical Statement

All procedures followed were in accordance with the ethical standards.

Supporting Institution

No financial support has been received for conducting the research and/or for the preparation of the article.

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