Advancing Architectural Floorplan Design with Geometry-enhanced Graph Diffusion

• URL: http://arxiv.org/abs/2408.16258v1
• Date: Thu, 29 Aug 2024 04:40:31 GMT
• Title: Advancing Architectural Floorplan Design with Geometry-enhanced Graph Diffusion
• Authors: Sizhe Hu, Wenming Wu, Yuntao Wang, Benzhu Xu, Liping Zheng,
• Abstract summary: We propose a novel generative framework for vector design via structural graph generation, called GSDiff. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model's perception of the overall geometric structure.
• Score: 3.78198085695976
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating architectural floorplan design is vital for housing and interior design, offering a faster, cost-effective alternative to manual sketches by architects. However, existing methods, including rule-based and learning-based approaches, face challenges in design complexity and constrained generation with extensive post-processing, and tend to obvious geometric inconsistencies such as misalignment, overlap, and gaps. In this work, we propose a novel generative framework for vector floorplan design via structural graph generation, called GSDiff, focusing on wall junction generation and wall segment prediction to capture both geometric and semantic aspects of structural graphs. To improve the geometric rationality of generated structural graphs, we propose two innovative geometry enhancement methods. In wall junction generation, we propose a novel alignment loss function to improve geometric consistency. In wall segment prediction, we propose a random self-supervision method to enhance the model's perception of the overall geometric structure, thereby promoting the generation of reasonable geometric structures. Employing the diffusion model and the Transformer model, as well as the geometry enhancement strategies, our framework can generate wall junctions, wall segments and room polygons with structural and semantic information, resulting in structural graphs that accurately represent floorplans. Extensive experiments show that the proposed method surpasses existing techniques, enabling free generation and constrained generation, marking a shift towards structure generation in architectural design.

Related papers

• FRI-Net: Floorplan Reconstruction via Room-wise Implicit Representation [18.157827697752317]
  We introduce a novel method called FRI-Net for 2D floorplan reconstruction from 3D point cloud. By incorporating geometric priors of room layouts in floorplans into our training strategy, the generated room polygons are more geometrically regular. Our method demonstrates improved performance compared to state-of-the-art methods, validating the effectiveness of our proposed representation for floorplan reconstruction.
  arXiv  Detail & Related papers  (2024-07-15T13:01:44Z)
• SE-VGAE: Unsupervised Disentangled Representation Learning for Interpretable Architectural Layout Design Graph Generation [0.0]
  We introduce an unsupervised disentangled representation learning framework, Style-based Edge-augmented Variational Graph Auto-Encoder. The framework generates architectural layout in the form of attributed adjacency multi-graphs while prioritizing representation disentanglement. We contribute a new benchmark large-scale architectural layout graph dataset extracted from real-world floor plan images.
  arXiv  Detail & Related papers  (2024-06-25T09:40:47Z)
• A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications [67.33002207179923]
  This paper presents a survey of data structures, models, and applications related to geometric GNNs. We provide a unified view of existing models from the geometric message passing perspective. We also summarize the applications as well as the related datasets to facilitate later research for methodology development and experimental evaluation.
  arXiv  Detail & Related papers  (2024-03-01T12:13:04Z)
• A Theory of Topological Derivatives for Inverse Rendering of Geometry [87.49881303178061]
  We introduce a theoretical framework for differentiable surface evolution that allows discrete topology changes through the use of topological derivatives. We validate the proposed theory with optimization of closed curves in 2D and surfaces in 3D to lend insights into limitations of current methods.
  arXiv  Detail & Related papers  (2023-08-19T00:55:55Z)
• Exploring Data Geometry for Continual Learning [64.4358878435983]
  We study continual learning from a novel perspective by exploring data geometry for the non-stationary stream of data. Our method dynamically expands the geometry of the underlying space to match growing geometric structures induced by new data. Experiments show that our method achieves better performance than baseline methods designed in Euclidean space.
  arXiv  Detail & Related papers  (2023-04-08T06:35:25Z)
• Dynamically Grown Generative Adversarial Networks [111.43128389995341]
  We propose a method to dynamically grow a GAN during training, optimizing the network architecture and its parameters together with automation. The method embeds architecture search techniques as an interleaving step with gradient-based training to periodically seek the optimal architecture-growing strategy for the generator and discriminator.
  arXiv  Detail & Related papers  (2021-06-16T01:25:51Z)
• Hermitian Symmetric Spaces for Graph Embeddings [0.0]
  We learn continuous representations of graphs in spaces of symmetric matrices over C. These spaces offer a rich geometry that simultaneously admits hyperbolic and Euclidean subspaces. The proposed models are able to automatically adapt to very dissimilar arrangements without any apriori estimates of graph features.
  arXiv  Detail & Related papers  (2021-05-11T18:14:52Z)
• Roof-GAN: Learning to Generate Roof Geometry and Relations for Residential Houses [37.6686237027665]
  Roof-GAN is a novel generative adversarial network that generates structured geometry of residential roof structures as a set of roof primitives and their relationships. The generator produces a structured roof model as a graph, which consists of 1) primitive geometry as images at each node, 2) inter-primitive colinear/coplanar relationships at each edge, and 3) primitive geometry in a vector format at each node. The discriminator is trained to assess the primitive geometry, the primitive relationships, and the primitive vector geometry in a fully end-to-end architecture.
  arXiv  Detail & Related papers  (2020-12-17T00:47:57Z)
• DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation [98.96086261213578]
  We introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes. This supports a range of novel shape generation applications with disentangled control, such as of structure (geometry) while keeping geometry (structure) unchanged. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-12T17:06:51Z)
• House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation [59.86153321871127]
  The main idea is to encode the constraint into the graph structure of its relational networks. We have demonstrated the proposed architecture for a new house layout generation problem.
  arXiv  Detail & Related papers  (2020-03-16T03:16:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.