MOO: A Multi-view Oriented Observations Dataset for Viewpoint Analysis in Cattle Re-Identification

Researchers introduced MOO, a large-scale synthetic dataset of 1,000 cattle individuals captured from 128 viewpoints to improve animal re-identification across different viewing angles. The dataset addresses critical challenges in aerial-ground re-identification by providing precise angular annotations and demonstrates effective transfer to real-world applications.

• →MOO dataset contains 128,000 annotated images of 1,000 cattle individuals from 128 uniformly sampled viewpoints.
• →The research identifies a critical elevation threshold above which models generalize better to unseen views.
• →Synthetic geometric priors effectively bridge the domain gap between synthetic and real-world data.
• →The dataset demonstrates performance gains across four real-world cattle datasets in both zero-shot and supervised settings.
• →MOO is publicly available and aims to advance cross-view animal re-identification model development.