A Scene-Context-Aware Texture Privacy-Preserving Method for Photogrammetric 3D Urban Models

Texture privacy preservation is a key technique for enabling the secure sharing and compliant use of three-dimensional (3D) urban models. However, maintaining visually continuous textures after privacy-preserving processing remains a challenging task due to the fragmented storage of textures in models. Considering the continuity of texture representation within the 3D scene, this paper proposes a scene-context-aware texture privacy-preserving method for 3D urban models. Specifically, a texture fragmentation metric is first introduced to adaptively determine the optimal detection level for sensitive targets, enabling accurate localization and segmentation of sensitive regions. Subsequently, scene-level texture contextual information associated with the detected regions is incorporated to guide a scene-aware texture repair strategy, achieving spatially consistent texture reconstruction. Furthermore, a multi-level texture pyramid mapping mechanism is established to ensure texture representation consistency across different levels of detail of the model. Experimental results demonstrate that the proposed method improves post-preservation texture continuity by approximately 56.9–79.5% compared with representative approaches that rely on fragmented texture map, significantly enhancing the usability and reliability of privacy-preserving results. Overall, this work provides a novel technical framework for privacy-preserving processing of 3D urban models and offers new insights for secure data sharing in smart city applications.