Comparative Study of Vision-Based Metric Measurement for Large-Scale Planar Scenes
It provides a practical comparison of measurement methods for large-scale outdoor monitoring, but the findings are incremental and domain-specific.
This paper compares three vision-based approaches for metric measurement in large-scale planar scenes, finding that monocular ranging achieves meter-level accuracy, stereo-based ranging achieves decimeter-level accuracy, and image stitching is effective only for small-scale scenes.
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 derived from camera geometry and the effect of camera pitch angle is analyzed. Image stitching is investigated for large-area mapping, while a stereo-based scheme is developed for long-range measurement without dedicated stereo hardware. Experiments show clear trade-offs: monocular ranging achieves meter-level accuracy under sufficiently large pitch angles, stereo-based ranging achieves decimeter-level accuracy with reduced sensitivity to pitch variations, and image stitching is effective for small-scale scenes but degrades in stability and scalability as scene size increases.