Photometric Consistency for Precise Drone Rephotography

Hsaun-Jui Chang, Tzu-Chun Huang, Hao-Liang Xu, Kuan-Wen Chen

Abstract: This paper proposes a precise drone rephotography system for fixed-domain patrolling scenarios. The proposed system integrates computer-vision-based localization and fine-tuned pixel-level dense flow prediction to achieve consistent and precise rephotography images with viewpoints that closely align with those of target images. The proposed Keypoints Alignment Through Dense Flow Prediction (KADFP) model effectively handles challenges arising from lighting variations and background differences. Moreover, a novel flight procedure is implemented in the proposed system. This procedure involves using an Interleaved Drone Controller to alternate between translation and rotation adjustments to ensure smooth flight dynamics during rephotography. Experiments indicated that the proposed system provided considerably more precise rephotography results (error of 4.72 pixels indoors) than did an existing localization approach (error of 35.56 pixels).