PBF-FR: Partitioning beyond footprints for façade recognition in urban point clouds

The identification and recognition of urban features are essential for creating accurate and comprehensive digital representations of cities. In particular, the automatic characterization of façade elements plays a key role in enabling semantic enrichment and 3D reconstruction. It also supports urban analysis and underpins various applications, including planning, simulation, and visualization. This work presents a pipeline for the automatic recognition of façades within complex urban scenes represented as point clouds. The method employs an enhanced partitioning strategy that extends beyond strict building footprints by incorporating surrounding buffer zones, allowing for a more complete capture of façade geometry, particularly in dense urban contexts. This is combined with a primitive recognition stage based on the Hough transform, enabling the detection of both planar and curved façade structures. The proposed partitioning overcomes the limitations of traditional footprint-based segmentation, which often disregards contextual geometry and leads to misclassifications at building boundaries. Integrated with the primitive recognition step, the resulting pipeline is robust to noise and incomplete data, and supports geometry-aware façade recognition, contributing to scalable analysis of large-scale urban environments. • PBF-FR is a pipeline for façade recognition within complex urban scenes represented as point clouds. • PBF-FR combines an improved spatial partitioning strategy with a primitive fitting approach based on the Hough Transform (HT). • The partitioning strategy allows a more complete capture of façade geometry by extending beyond strict building footprints with surrounding buffer zones. • The buffer zone depends on the arrangement of neighboring structures, without fixed size or thickness. • The HT-based recognition approach enables the detection of both planar and curved façade structures.