A recent study published on arXiv explores the utility of Euler angles as a rotation parameterization method for pose estimation in 3D computer vision. The research emphasizes the benefits of generating probabilistic pose outputs, which are particularly valuable in situations where pose ambiguity arises due to various constraints. By investigating Euler angles within the framework of Normalizing Flows, the paper contributes to understanding how rotation representations impact pose estimation accuracy and reliability. This approach aligns with ongoing efforts in the computer vision community to improve pose estimation techniques by incorporating uncertainty measures. The findings suggest that probabilistic outputs can enhance pose estimation performance when deterministic methods face challenges from ambiguous data. Overall, the study provides insights into the application domain of 3D pose estimation, highlighting the relevance of rotation parameterization choices in advanced AI models. This work complements recent research trends focusing on probabilistic modeling to address inherent uncertainties in pose estimation tasks.