FGSSNet: Feature-Guided Semantic Segmentation of Real World Floorplans

2025/07/14 by Hugo Norrby, Gabriel Färm, Norrby, Hugo +5
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Handwritten Text Recognition Techniques #Image Processing and 3D Reconstruction

paper · pdf · doi:10.48550/arxiv.2507.10343

openalex publication_date 2025/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce FGSSNet, a novel multi-headed feature-guided semantic segmentation (FGSS) architecture designed to improve the generalization ability of wall segmentation on floorplans. FGSSNet features a U-Net segmentation backbone with a multi-headed dedicated feature extractor used to extract domain-specific feature maps which are injected into the latent space of U-Net to guide the segmentation process. This dedicated feature extractor is trained as an encoder-decoder with selected wall patches, representative of the walls present in the input floorplan, to produce a compressed latent representation of wall patches while jointly trained to predict the wall width. In doing so, we expect that the feature extractor encodes texture and width features of wall patches that are useful to guide the wall segmentation process. Our experiments show increased performance by the use of such injected features in comparison to the vanilla U-Net, highlighting the validity of the proposed approach.

Citations

• SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
• Per-Pixel Classification is Not All You Need for Semantic Segmentation
• SegFormer: Simple and Efficient Design for Semantic Segmentation with\n Transformers
• CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan\n Image Analysis
• Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
• Rethinking Atrous Convolution for Semantic Image Segmentation
• U-Net: Convolutional Networks for Biomedical Image Segmentation
• Fully Convolutional Networks for Semantic Segmentation
• Generation of 3D building models from 2D architectural plans

Related

• Enhancing Floor Plan Recognition: A Hybrid Mix-Transformer and U-Net Approach for Precise Wall Segmentation
• CAGE: Continuity-Aware edGE Network Unlocks Robust Floorplan Reconstruction
• The Direction-Aware, Learnable, Additive Kernels and the Adversarial Network for Deep Floor Plan Recognition
• Parsing Line Segments of Floor Plan Images Using Graph Neural Networks
• Feature Pyramid Encoding Network for Real-time Semantic Segmentation