具有高效可计算 Bingham 损失函数的概率旋转表示及其在姿态估计中的应用
Probabilistic Rotation Representation With an Efficiently Computable Bingham Loss Function and Its Application to Pose Estimation
AI总结：
针对四元数无法表示旋转不确定性而 Bingham 分布归一化常数计算复杂的问题，本文提出一种快速、易实现的 Bingham 损失函数，并验证其参数化与姿态估计应用。
摘要：
近年来，深度学习框架已被广泛用于物体姿态估计。虽然四元数是 6D 姿态旋转表示的常见选择，但它无法表示观测的不确定性。为了处理这种不确定性，Bingham 分布是一种很有前景的解决方案，因为它具有合适的特性，例如除了能够表示歧义性之外，还能在 SO(3) 上进行平滑表示。然而，它需要对归一化常数进行复杂计算。这是在训练基于 Bingham 表示的神经网络时损失计算的瓶颈。因此，我们提出了一种计算快速且易于实现的 Bingham 分布损失函数。我们还表明，不仅要检验 Bingham 分布的参数化，还要展示基于我们所提损失函数的应用。
英文摘要：
In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation of 6D pose, it cannot represent an uncertainty of the observation. In order to handle the uncertainty, Bingham distribution is one promising solution because this has suitable features, such as a smooth representation over SO(3), in addition to the ambiguity representation. However, it requires the complex computation of the normalizing constants. This is the bottleneck of loss computation in training neural networks based on Bingham representation. As such, we propose a fast-computable and easy-to-implement loss function for Bingham distribution. We also show not only to examine the parametrization of Bingham distribution but also an application based on our loss function.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
