Adapting HouseDiffusion for conditional Floor Plan generation on Modified Swiss Dwellings dataset (2312.03938v1)
Abstract: Automated floor plan generation has recently gained momentum with several methods that have been proposed. The CVAAD Floor Plan Auto-Completion workshop challenge introduced MSD, a new dataset that includes existing structural walls of the building as an additional input constraint. This technical report presents an approach for extending a recent work, HouseDiffusion (arXiv:2211.13287 [cs.CV]), to the MSD dataset. The adaption involves modifying the model's transformer layers to condition on a set of wall lines. The report introduces a pre-processing pipeline to extract wall lines from the binary mask of the building structure provided as input. Additionally, it was found that a data processing procedure that simplifies all room polygons to rectangles leads to better performance. This indicates that future work should explore better representations of variable-length polygons in diffusion models. The code will be made available at a later date.
- Fast graph representation learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds, 2019.
- House-gan++: Generative adversarial layout refinement networks, 2021.
- Housediffusion: Vector floorplan generation via a diffusion model with discrete and continuous denoising, 2022.
- Graph attention networks, 2018.
- Data-driven interior plan generation for residential buildings. ACM Transactions on Graphics (TOG), 38(6):1–12, 2019.
- Yan xiaolong. Skeleton network. https://github.com/Image-Py/sknw, 2021.
- Emanuel Kuhn (2 papers)