Abstract
Animal re-identification (ReID) faces critical challenges due to viewpoint variations, particularly in Aerial-Ground (AG-ReID) settings where models must match individuals across drastic elevation changes. However, existing datasets lack the precise angular annotations required to systematically analyze these geometric variations. To address this, we introduce the Multi-view Oriented Observation (MOO) dataset, a large-scale synthetic AG-ReID dataset of $1,000$ cattle individuals captured from $128$ uniformly sampled viewpoints ($128,000$ annotated images). Using this controlled dataset, we quantify the influence of elevation and identify a critical elevation threshold, above which models generalize significantly better to unseen views. Finally, we validate the transferability to real-world applications in both zero-shot and supervised settings, demonstrating performance gains across four real-world cattle datasets and confirming that synthetic geometric priors effectively bridge the domain gap. Collectively, this dataset and analysis lay the foundation for future model development in cross-view animal ReID. MOO is publicly available at this https URL.
Abstract (translated)
动物再识别（ReID）在不同视角变化下面临重大挑战，尤其是在空地结合（AG-ReID）场景中，模型必须匹配高度变化显著条件下的个体。然而，现有的数据集缺乏精确的角度标注，无法系统分析这些几何变化。为解决这一问题，我们推出了多视图定向观测（MOO）数据集，这是一个大规模的合成AG-ReID数据集，包含1,000头牛个体，从128个均匀采样的视角捕获，总计有128,000张标注图像。利用这个受控的数据集，我们量化了高度变化的影响，并确定了一个临界的高度阈值，在此之上模型能够更好地泛化到未见过的视图中。最后，我们在零样本和监督设置下验证了其在四个真实世界牛数据集中向实际应用迁移的能力，证明在所有这些数据集中性能均有提升，并确认合成几何先验有效地弥合了域间差距。总的来说，该数据集及其分析为未来跨视角动物ReID模型的发展奠定了基础。MOO数据集可在此网址公开获取：[https://此链接应替换为实际发布地址]。
URL
https://arxiv.org/abs/2603.04314
