关键词: 古建筑, 单目深度估计, 无监督学习, 局部向心推演, 深度混淆

Abstract: To address the depth estimation confusion problem caused by the lack of spatial correlation mechanisms and local feature decoupling capabilities in existing methods for ancient architecture scenes with interleaved spatial hierarchies and dense edges, an unsupervised monocular depth estimation method based on local centripetal deduction was proposed. First, an encoder was adopted to extract multi-scale features from the input image. A local centripetal deduction strategy was constructed to reshape the spatial structure of features by utilizing the spatial correlation between the central pixel and neighboring pixels within a local attention window. In addition, a feature decoupling strategy based on a gated screening mechanism was introduced to reconstruct the encoder features, thereby effectively alleviating the depth estimation confusion problem. Then, by integrating the two aforementioned strategies, a depth estimation network based on local centripetal deduction was constructed to achieve the depth parsing of complex spatial hierarchies and the fine preservation of dense edges in ancient architecture scenes. Finally, under the unsupervised learning framework, the minimum reprojection loss and the edge-aware smoothness loss were adopted to realize the end-to-end joint optimization of the method. The experimental results on a self-built ancient architecture dataset show that, compared with Monodepth2, which performs the best among the comparison methods, the absolute relative error of the proposed method is reduced by 4.86%, and the accuracy (threshold $\delta < 1.25$) is increased by 0.64 percentage points. The method effectively overcomes the depth feature confusion problem brought by interleaved structures and dense edges, and significantly improves the three-dimensional (3D) perception capability for complex geometric details of ancient architectures. It provides precise geometric priors and technical support for the component-level high-fidelity mapping, non-destructive spatial analysis, and 3D digital archiving of ancient architectures.

Key words: ancient architectures, monocular depth estimation, unsupervised learning, local centripetal deduction, depth confusion