ZeD-MAP: Bundle Adjustment Guided Zero-Shot Depth Maps for Real-Time Aerial Imaging
Selim Ahmet Iz, Francesco Nex, Norman Kerle, Henry Meissner, Ralf Berger

TL;DR
ZeD-MAP introduces a real-time, cluster-based bundle adjustment framework that enhances zero-shot diffusion models for accurate, consistent depth mapping in UAV aerial imaging under strict computational constraints.
Contribution
It presents a novel integration of incremental cluster-based bundle adjustment with diffusion depth models to achieve real-time, metrically consistent 3D mapping from UAV imagery.
Findings
Achieves sub-meter accuracy in depth estimation.
Maintains per-image runtime between 1.47 and 4.91 seconds.
Provides metric guidance comparable to classical photogrammetry.
Abstract
Real-time depth reconstruction from ultra-high-resolution UAV imagery is essential for time-critical geospatial tasks such as disaster response, yet remains challenging due to wide-baseline parallax, large image sizes, low-texture or specular surfaces, occlusions, and strict computational constraints. Recent zero-shot diffusion models offer fast per-image dense predictions without task-specific retraining, and require fewer labelled datasets than transformer-based predictors while avoiding the rigid capture geometry requirement of classical multi-view stereo. However, their probabilistic inference prevents reliable metric accuracy and temporal consistency across sequential frames and overlapping tiles. We present ZeD-MAP, a cluster-level framework that converts a test-time diffusion depth model into a metrically consistent, SLAM-like mapping pipeline by integrating incremental…
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