HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image Priors

Ashkan Ganj, Hang Su, Tian Guo

Abstract: We propose Hybriddepth, a robust depth estimation pipeline that addresses key challenges in depth estimation, including scale ambiguity, hardware heterogene-ity, and generalizability. Hybriddepth leverages focal stack, data conveniently accessible in common mobile de-vices, to produce accurate metric depth maps. By incorpo-rating depth priors afforded by recent advances in single-image depth estimation, our model achieves a higher level of structural detail compared to existing methods. We test our pipeline as an end-to-end system, with a newly developed mobile client to capture focal stacks, which are then sent to a GPU-powered server for depth estimation. Comprehensive quantitative and qualitative analyses demonstrate that Hybriddepth outperforms state-of-the-art (SOTA) models on common datasets such as DDFF12 and NYU Depth V2. Hybriddepth also shows strong zero-shot generalization. When trained on NYU Depth V2, Hybriddepth surpasses SOTA models in zero-shot per-formance on ARKitScenes and delivers more structurally accurate depth maps on Mobile Depth. The code is avail-able at https://github.com/cake-labIHybridDepth/.

External IDs:dblp:conf/wacv/GanjS025