论文标题
概率旋转表示具有有效计算的宾厄姆损失函数及其在姿势估计中的应用
Probabilistic Rotation Representation With an Efficiently Computable Bingham Loss Function and Its Application to Pose Estimation
论文作者
论文摘要
近年来，深度学习框架已被广泛用于对象姿势估计。虽然四元素是6D姿势旋转表示的共同选择，但它不能代表观察结果的不确定性。为了处理不确定性，宾厄姆分布是一个有前途的解决方案，因为除了歧义表示外，它具有合适的特征，例如SO（3）的平滑表示。但是，它需要对标准化常数的复杂计算。这是基于宾厄姆代表的训练神经网络中的损失计算的瓶颈。因此，我们为宾厄姆分布提出了可快速且易于实现的损失函数。我们还显示，不仅要检查宾厄姆分布的参数化，而且还基于我们的损失函数来检查应用程序。
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.
