用欧拉角构建流模型，提升3D姿态估计的不确定性建模能力。
Are Euler angles a useful rotation parameterisation for pose estimation with Normalizing Flows?
- 以欧拉角为参数化基础，构建归一化流模型进行姿态估计。
- 在对称物体和模糊观测下，相比复杂参数化更稳定可靠。
- 适合需要概率输出的复杂场景姿态估计任务。
3D计算机视觉中的目标姿态估计至关重要。当姿态因传感器限制或物体对称性而不确定时，概率性输出比单点估计更具优势。本文探索将经典的欧拉角参数化作为归一化流模型的基础，用于姿态估计。尽管欧拉角存在奇点等缺陷，但在某些方面可能优于更复杂的参数化方式。研究发现，基于欧拉角的模型在处理对称物体和遮挡场景时表现更稳健，能有效捕捉姿态分布的多模态特性，尤其适用于需要不确定性建模的实际应用。
原文摘要 · Abstract (English)
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.
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