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

arXiv: https://arxiv.org/abs/2407.18443 · HTML: https://mlsanity.com/paper/2407.18443/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 32 references that are in the map.
- First posted on arXiv 26 Jul 2024.

## Builds on (most cited first, 23 of 23)

- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [ZoeDepth: Zero-shot Transfer by Combining Relative and Metric Depth](https://mlsanity.com/paper/2302.12288/) (2023)
- [AdaBins: Depth Estimation using Adaptive Bins](https://mlsanity.com/paper/2011.14141/) (2020)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [Unleashing Text-to-Image Diffusion Models for Visual Perception](https://mlsanity.com/paper/2303.02153/) (2023)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [Towards Zero-Shot Scale-Aware Monocular Depth Estimation](https://mlsanity.com/paper/2306.17253/) (2023)
- [LocalBins: Improving Depth Estimation by Learning Local Distributions](https://mlsanity.com/paper/2203.15132/) (2022)
- [ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation](https://mlsanity.com/paper/2403.18807/) (2024)
- [Toward Practical Monocular Indoor Depth Estimation](https://mlsanity.com/paper/2112.02306/) (2021)
- [Focus on defocus: bridging the synthetic to real domain gap for depth estimation](https://mlsanity.com/paper/2005.09623/) (2020)
- [Deep Depth From Focus](https://mlsanity.com/paper/1704.01085/) (2017)
- [Bridging Unsupervised and Supervised Depth from Focus via All-in-Focus Supervision](https://mlsanity.com/paper/2108.10843/) (2021)
- [Deep Depth from Focus with Differential Focus Volume](https://mlsanity.com/paper/2112.01712/) (2021)
- [Monocular Visual-Inertial Depth Estimation](https://mlsanity.com/paper/2303.12134/) (2023)
- [Fully Self-Supervised Depth Estimation from Defocus Clue](https://mlsanity.com/paper/2303.10752/) (2023)
- [Variational Depth from Focus Reconstruction](https://mlsanity.com/paper/1408.0173/) (2014)
- [Learning Depth from Focus in the Wild](https://mlsanity.com/paper/2207.09658/) (2022)
- [DeepPointMap: Advancing LiDAR SLAM with Unified Neural Descriptors](https://mlsanity.com/paper/2312.02684/) (2023)
- [Attention and Sensory Processing in Augmented Reality: Empowering ADHD population](https://mlsanity.com/paper/2405.01218/) (2024)

## Built on by (most cited first, 11 of 11)

- [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025)
- [HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model](https://mlsanity.com/paper/2510.05054/) (2025)
- [SingRef6D: Monocular Novel Object Pose Estimation with a Single RGB Reference](https://mlsanity.com/paper/2509.21927/) (2025)
- [DualFocus: Depth from Focus with Spatio-Focal Dual Variational Constraints](https://mlsanity.com/paper/2509.21992/) (2025)
- [AR as an Evaluation Playground: Bridging Metrics and Visual Perception of Computer Vision Models](https://mlsanity.com/paper/2508.04102/) (2025)
- [SwiftNDC: Fast Neural Depth Correction for High-Fidelity 3D Reconstruction](https://mlsanity.com/paper/2602.22565/) (2026)
- [Long-Term Visual Localization in Dynamic Benthic Environments: A Dataset, Footprint-Based Ground Truth, and Visual Place Recognition Benchmark](https://mlsanity.com/paper/2603.04056/) (2026)
- [Zero-Shot Depth from Defocus](https://mlsanity.com/paper/2603.26658/) (2026)
- [Towards Minimal Focal Stack in Shape from Focus](https://mlsanity.com/paper/2604.01603/) (2026)
- [Enhancing Hazy Wildlife Imagery: AnimalHaze3k and IncepDehazeGan](https://mlsanity.com/paper/2604.16284/) (2026)
- [Enhancing Visual Perception in Foggy Conditions via Multiclass Fog Density Modeling](https://mlsanity.com/paper/2608.01572/) (2026)

## Abstract (arXiv)

We propose HYBRIDDEPTH, a robust depth estimation pipeline that addresses key challenges in depth estimation,including scale ambiguity, hardware heterogeneity, and generalizability. HYBRIDDEPTH leverages focal stack, data conveniently accessible in common mobile devices, to produce accurate metric depth maps. By incorporating depth priors afforded by recent advances in singleimage 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 performance on ARKitScenes and delivers more structurally accurate depth maps on Mobile Depth. The code is available at https://github.com/cake-lab/HybridDepth/.