CG-Floor: Centroid-guided diffusion for large-scale floorplan generation

Lian, Hongjin, Ma, Jian, Chen, Hongjie, Li, Jia, Hu, Ruizhen, Lai, Yukun ORCID: https://orcid.org/0000-0002-2094-5680 and Li, Kun 2026. CG-Floor: Centroid-guided diffusion for large-scale floorplan generation. Presented at: The IEEE/CVF Conference on Computer Vision and Pattern Recognition 2026, Denver, CO, USA, 3-7 June 2026. IEEE,

PDF - Accepted Post-Print Version Download (2MB)

Abstract

Large-scale floorplan generation is critical for virtual space planning and architectural simulation. Although existing methods have shown success in generating smallscale floorplans with simple room shapes, they struggle to handle complex room connections and irregular room shapes that arise in large-scale floorplans. In this paper, we propose CG-Floor, a centroid-guided hierarchical framework that explicitly decouples room position and shape generation to address these issues. We first introduce the sizeaware semantic centroid heatmap, derived from predicted room centroids and sizes, which provides a structured representation to guide the effective generation of a coarseto- fine floorplan generator while ensuring semantic alignment. Additionally, we train a vector quantized codebook of floorplans with complex room shapes to capture the diversity of room shapes and employ a latent diffusion transformer to generate large-scale floorplans featuring non- Manhattan room shapes. CG-Floor achieves state-of-theart performance on the large-scale MSD dataset, and supports 3D floorplan conversion and editing, demonstrating the practicality of our approach. The code is available at https://cic.tju.edu.cn/faculty/likun/projects/CG-Floor.

Item Type:	Conference or Workshop Item - published (Paper)
Status:	In Press
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
Funders:	ESPRC
Projects:	EP/Y028805/1
Date of First Compliant Deposit:	13 May 2026
Date of Acceptance:	21 February 2026
Last Modified:	15 Jul 2026 13:00
URI:	https://orca.cardiff.ac.uk/id/eprint/186972

Actions (repository staff only)

Edit Item