Object pose estimation is a task that is of central importance in 3D Computer Vision. Given a target image and a canonical pose, a single point estimate may very often be sufficient; however, a probabilistic pose output is related to a number of benefits when pose is not unambiguous due to sensor and projection constraints or inherent object symmetries. With this paper, we explore the usefulness of using the well-known Euler angles parameterisation as a basis for a Normalizing Flows model for pose estimation. Isomorphic to spatial rotation, 3D pose has been parameterized in a number of ways, either in or out of the context of parameter estimation. We explore the idea that Euler angles, despite their shortcomings, may lead to useful models in a number of aspects, compared to a model built on a more complex parameterisation.

翻译：物体姿态估计是三维计算机视觉中的核心任务。给定目标图像和规范姿态，单点估计通常已足够；然而，当因传感器与投影约束或物体固有对称性导致姿态不明确时，概率化姿态输出具有诸多优势。本文探讨了以经典欧拉角参数化作为归一化流姿态估计模型基础的有效性。三维姿态与空间旋转同构，在参数估计的语境内外已存在多种参数化方式。我们探究以下观点：尽管存在缺陷，相较于基于更复杂参数化的模型，欧拉角可能在多个维度上导向更具实用价值的模型。