Efficient and Precise Drone Rephotography for Video Sequences

Abstract

Precise drone rephotography technology aims to recover camera poses from a reference sequence and obtain well-aligned image sequences, playing a crucial role in autonomous drone inspection tasks. However, existing rephotography methods rely on static image inputs, resulting in low efficiency and limited applicability in real-world scenarios. This paper presents a novel video-based precise drone rephotography system leveraging video sequences. To the best of our knowledge, this is the first work to extend precise drone rephotography from still images to videos while significantly reducing rephotography time. The proposed approach integrates advanced visual SLAM techniques with a dense flow prediction model to continuously refine the drone’s pose, enabling robust and precise rephotography tasks. To further quantify system performance, we introduce a trajectory-based visual similarity evaluation standard—Dynamic Frame Alignment Error (DFAE), which assesses the visual similarity of drone-captured videos of varying durations. We conducted multiple experiments with drones in real-world scenarios. Experimental results demonstrate that the proposed system achieves efficient and precise rephotography across multiple indoor and outdoor trials. Specifically, the average rephotography error is only 7.956 pixels indoors and 9.800 pixels outdoors. More importantly, the rephotography time is only half of the baseline.