Toward Robust Depth Fusion for Mobile AR With Depth from Focus and Single-Image Priors

We propose HybridDepth, a robust depth estimation pipeline that addresses the unique challenges of depth estimation for mobile AR, such as scale ambiguity, hardware heterogeneity, and generalizability.HybridDepthleverages the camera features available on mobile devices. It effectively combines the scale accuracy inherent in Depth from Focus (DFF) methods with the generalization capabilities enabled by strong single-image depth priors. By utilizing the focal planes of a mobile camera, our approach accurately captures depth values from focused pixels and applies these values to compute scale and shift parameters for transforming relative depths into metric depths. Through comprehensive quantitative and qualitative analyses, we demonstrate that HybridDepthnot only outperforms state-of-the-art (SOTA) models in common datasets (DDFF12, NYU Depth v2). The source code of this project is available at https://github.com/cake-lab/HybridDepth.