UAVPairs: A Challenging Benchmark for Match Pair Retrieval of Large-scale UAV Images

TL;DR

This paper introduces UAVPairs, a challenging large-scale UAV image dataset and a novel training pipeline that significantly improves match pair retrieval accuracy, robustness, and 3D reconstruction quality in diverse and challenging scenes.

Contribution

The paper presents UAVPairs, a new benchmark dataset, a batched nontrivial sample mining strategy, and a ranked list loss for enhanced UAV image retrieval.

Findings

Models trained on UAVPairs outperform existing datasets in retrieval accuracy.

The approach improves 3D model reconstruction quality.

Enhanced robustness in challenging textured scenes.

Abstract

The primary contribution of this paper is a challenging benchmark dataset, UAVPairs, and a training pipeline designed for match pair retrieval of large-scale UAV images. First, the UAVPairs dataset, comprising 21,622 high-resolution images across 30 diverse scenes, is constructed; the 3D points and tracks generated by SfM-based 3D reconstruction are employed to define the geometric similarity of image pairs, ensuring genuinely matchable image pairs are used for training. Second, to solve the problem of expensive mining cost for global hard negative mining, a batched nontrivial sample mining strategy is proposed, leveraging the geometric similarity and multi-scene structure of the UAVPairs to generate training samples as to accelerate training. Third, recognizing the limitation of pair-based losses, the ranked list loss is designed to improve the discrimination of image retrieval models, which optimizes the global similarity structure constructed from the positive set and negative set. Finally, the effectiveness of the UAVPairs dataset and training pipeline is validated through comprehensive experiments on three distinct large-scale UAV datasets. The experiment results demonstrate that models trained with the UAVPairs dataset and the ranked list loss achieve significantly improved retrieval accuracy compared to models trained on existing datasets or with conventional losses. Furthermore, these improvements translate to enhanced view graph connectivity and higher quality of reconstructed 3D models. The models trained by the proposed approach perform more robustly compared with hand-crafted global features, particularly in challenging repetitively textured scenes and weakly textured scenes. For match pair retrieval of large-scale UAV images, the trained image retrieval models offer an effective solution. The dataset would be made publicly available at https://github.com/json87/UAVPairs.