# Zero-Shot Metric Depth Estimation via Monocular Visual-Inertial Rescaling for Autonomous Aerial Navigation

arXiv: https://arxiv.org/abs/2509.08159 · HTML: https://mlsanity.com/paper/2509.08159/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 9 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [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)
- [Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation](https://mlsanity.com/paper/2404.15506/) (2024)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image](https://mlsanity.com/paper/2307.10984/) (2023)
- [UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler](https://mlsanity.com/paper/2502.20110/) (2025)
- [Flightmare: A Flexible Quadrotor Simulator](https://mlsanity.com/paper/2009.00563/) (2020)
- [Monocular Visual-Inertial Depth Estimation](https://mlsanity.com/paper/2303.12134/) (2023)
- [Autonomous Cave Surveying with an Aerial Robot](https://mlsanity.com/paper/2003.13883/) (2020)
- [A Simple yet Effective Test-Time Adaptation for Zero-Shot Monocular Metric Depth Estimation](https://mlsanity.com/paper/2412.14103/) (2024)
- [Reactive Collision Avoidance for Safe Agile Navigation](https://mlsanity.com/paper/2409.11962/) (2024)
- [Rapid Quadrotor Navigation in Diverse Environments using an Onboard Depth Camera](https://mlsanity.com/paper/2411.04326/) (2024)
- [Time-Optimized Safe Navigation in Unstructured Environments through Learning Based Depth Completion](https://mlsanity.com/paper/2506.14975/) (2025)

## Built on by (most cited first, 2 of 2)

- [Boosting Metric Depth Completion via Training-Free Adaptive Response Geometry](https://mlsanity.com/paper/2609.36168/) (2026)
- [Harnessing SPAD Noise: Test-Time Gated Attention for Indoor Depth Estimation](https://mlsanity.com/paper/2610.15431/) (2026)

## Abstract (arXiv)

This paper presents a methodology to predict metric depth from monocular RGB images and an inertial measurement unit (IMU). To enable collision avoidance during autonomous flight, prior works either leverage heavy sensors (e.g., LiDARs or stereo cameras) or data-intensive and domain-specific fine-tuning of monocular metric depth estimation methods. In contrast, we propose several lightweight zero-shot rescaling strategies to obtain metric depth from relative depth estimates via the sparse 3D feature map created using a visual-inertial navigation system. These strategies are compared for their accuracy in diverse simulation environments. The best performing approach, which leverages monotonic spline fitting, is deployed in the real-world on a compute-constrained quadrotor. We obtain on-board metric depth estimates at 15 Hz and demonstrate successful collision avoidance after integrating the proposed method with a motion primitives-based planner.