A Probabilistic Rotation Representation for Symmetric Shapes With an Efficiently Computable Bingham Loss Function
Hiroya Sato,Takuya Ikeda,Koichi Nishiwaki,Hiroya Sato,Takuya Ikeda,Koichi Nishiwaki
In recent years, a deep learning framework has been widely used for object pose estimation. While quaternion is a common choice for rotation representation, it cannot represent the ambiguity of the observation. In order to handle the ambiguity, the Bingham distribution is one promising solution. However, it requires complicated calculation when yielding the negative log-likelihood (NLL) loss. An a...
