Biomimetic Cross-Scale Feature Recalibration with Axis-Decompositional Positional Embedding for Architectural Floor Plan Parsing

Automatic semantic parsing of architectural floor plans provides a two-dimensional semantic layer for building information modeling (BIM)-related workflows, intelligent plan checking, renovation, and large-scale drawing management. Unlike natural images, floor plans contain sparse textures, dense linework, small architectural symbols, and strong axis-aligned geometric regularities. These properties make conventional segmentation networks vulnerable to small-symbol dilution during downsampling, noisy skip-feature fusion, and fragmented predictions along long wall boundaries. Inspired by principles of hierarchical visual processing, selective attention, and spatial encoding, this study presents PCP-Net, an end-to-end Planar Component Parsing Network for room, icon, and boundary-aware floor plan parsing. PCP-Net uses a Grouped Residual Encoder to extract multi-scale local patterns, a Cross-Scale Feature Recalibration (CSFR) pipeline to recalibrate skip features through Adaptive Channel Gating, Spatial Response Amplification, and Axis-Decompositional Positional Embedding, and a Structural Gradient Propagation branch to provide training-time boundary regularization. Experiments on CubiCasa5K and three external datasets (R3D, CVC-FP, and ROBIN) evaluate PCP-Net under in-domain and zero-shot cross-domain protocols. On CubiCasa5K, PCP-Net attains 71.3% mIoU, 90.1% overall accuracy, and 78.9% mean accuracy; in the current ablation setting, ADPE improves mIoU by 0.8 percentage points over the ADPE-ablated configuration. These results indicate that cross-scale feature recalibration with axis-aware positional cues can improve floor plan semantic parsing within the evaluated datasets and pixel-level metrics, while downstream BIM generation still requires additional vectorization, topology graph construction, and attribute extraction.