Distortion-Aware Outdoor Panoramic Depth Estimation via Local–Global Fusion

Outdoor panoramic depth estimation faces significant challenges due to the wide field of view (FoV), complex scene structures, and severe distortion encountered in such environments. Traditional methods, which often use distortion convolution, fall short in capturing global distortion information and extracting rich contextual details from panoramic images. To overcome these limitations, this article introduces a novel dual-branch framework that synergistically merges the advantages of equirectangular projection (ERP) and tangent projection (TP). First, we design a unique dual-branch framework specifically tailored for panoramic depth estimation. In this framework, the convolutional neural networks branch processes ERP images to extract rich local information, enhancing the detail accuracy of depth estimation, while the vision transformers branch processes TPs to capture comprehensive global information, improving the smoothness of depth estimation. Then, we further enhance our method with a distortion-aware weight map module that adapts the influence of different image regions according to their distortion level, thus prioritizing features from areas with less distortion. In addition, we implement a dual attention fusion module to seamlessly integrate features from both branches at corresponding layers. Comprehensive experiments across various outdoor datasets reveal that our method significantly outperforms state-of-the-art techniques in terms of depth estimation accuracy, adeptly balancing the capture of both overarching scene depth and intricate details, potentially revolutionizing applications in industrial informatics, such as autonomous navigation and environmental mapping.