arXiv:2605.26475v1 Announce Type: cross Abstract: Vision-based metric distance and area measurement remains challenging in large-scale outdoor environments due to long-range sensing, camera zoom, and unstable imaging conditions. This work studies planar metric measurement in a real-world reservoir monitoring scenario using PTZ cameras and compares three representative approaches: geometry-based monocular ranging, image stitching with birds-eye-view transformation, and stereo-based ranging using two jointly calibrated monocular cameras. For monocular ranging, planar localization models are deri
The paper, published in 2026, details advancements in vision-based metric measurement, indicating ongoing progress in computer vision applications for complex environments.
This research addresses a persistent challenge in large-scale outdoor monitoring, which has implications for various sectors needing precise spatial data.
Improved reliability and precision in automated long-range measurement systems, particularly for dynamic and challenging outdoor settings, become more feasible.
- · Surveillance technology providers
- · Infrastructure monitoring companies
- · AI/Computer Vision researchers
- · Environmental monitoring agencies
- · Manual surveying methods
- · Companies reliant on less accurate measurement techniques
Enhanced capabilities for autonomous monitoring and mapping in challenging environments with PTZ cameras.
Reduced operational costs and increased efficiency for large-scale asset management and environmental oversight.
This could enable more sophisticated AI agent applications that require precise real-world spatial understanding for autonomous operations and decision-making.
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Read at arXiv cs.AI