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Unconstrained Large-scale 3D Reconstruction and Rendering across Altitudes

Published: April 29, 2025 | arXiv ID: 2505.00734v1

By: Neil Joshi , Joshua Carney , Nathanael Kuo and more

BigTech Affiliations: Johns Hopkins University

Potential Business Impact:

Creates 3D maps from few, messy photos.

Business Areas:
3D Technology Hardware, Software

Production of photorealistic, navigable 3D site models requires a large volume of carefully collected images that are often unavailable to first responders for disaster relief or law enforcement. Real-world challenges include limited numbers of images, heterogeneous unposed cameras, inconsistent lighting, and extreme viewpoint differences for images collected from varying altitudes. To promote research aimed at addressing these challenges, we have developed the first public benchmark dataset for 3D reconstruction and novel view synthesis based on multiple calibrated ground-level, security-level, and airborne cameras. We present datasets that pose real-world challenges, independently evaluate calibration of unposed cameras and quality of novel rendered views, demonstrate baseline performance using recent state-of-practice methods, and identify challenges for further research.

Country of Origin
🇺🇸 United States

Page Count
9 pages

Category
Computer Science:
CV and Pattern Recognition