构建首个带精确角度标注的牛只跨视角识别数据集，助力模型抗视角变化。
MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification
- 基于128个均匀采样视角生成1000头牛的合成数据，含精确角度标注。
- 发现视角高度超过阈值后模型泛化能力显著提升，验证了几何先验有效性。
- 可直接用于真实场景零样本与有监督迁移，适配农业智能监控需求。
动物重识别（ReID）在空地（AG-ReID）场景中面临视角剧烈变化的挑战。现有数据集缺乏精确的角度标注，难以系统分析几何变化影响。为此，我们提出多视角观测数据集MOO，包含1000头牛，从128个均匀采样的视角拍摄，共128,000张标注图像。利用该受控数据集，我们量化了高度变化对模型的影响，识别出一个关键高度阈值——超过此值，模型对未见视角的泛化能力显著增强。进一步在四个真实世界牛只数据集上验证，无论零样本或有监督设置下均实现性能提升，证明合成几何先验能有效弥合域间差距。该数据集与分析为跨视角动物ReID模型发展奠定基础。MOO已开源：https://github.com/TurtleSmoke/MOO。
原文摘要 · Abstract (English)
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 https://github.com/TurtleSmoke/MOO.
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