OccFaçade: enabling precise building façade parsing in large urban scenes with occlusion

Building façade parsing is to recognize the building façade image into different categories of individuals including walls, doors, windows, balconies, etc. However, obstructions such as trees present a significant challenge to conducting façade parsing. In this paper, we designed OccFaçade to achieve high-precision parsing of occluded building façades in large urban scenes. OccFaçade primarily incorporates two modules, Multi-layer Dilated Convolution Module (MD-Module) and Multi-scale Row-Column Convolution Module (MRC-Module), to capture repeated texture in local and row-column directions. This aims to leverage repetitive textures to address occlusion challenges in building façade parsing. Besides, we introduce our building façade dataset MeshFaçade from the Mesh data generated by drone imagery to study the occlusion problem of missing textures. The experimental results demonstrate that OccFaçade achieves state-of-the-art performance with mIOU of 85.01%, 84.09%, 72.95%, and 88.83% on the ENPC2014 dataset, ECP dataset, RueMonge2014 dataset, and our MeshFaçade dataset, respectively. The code and data are available at https://github.com/yueyisui/OccFacade.