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 alternative easy-to-implement loss function has been proposed to avoid complex computations but has difficulty expressing symmetric distribution. In this paper, we introduce a fast-computable and easy-to-implement NLL loss function for Bingham distribution. We also create the inference network and show that our loss function can capture the symmetric property of target objects from their point clouds.

翻译：近年来，深度学习框架已广泛应用于物体姿态估计任务。尽管四元数是旋转表示的常用选择，但它无法表征观测数据中的模糊性。为处理这种模糊性，宾厄姆分布提供了一种有前景的解决方案。然而，该分布在计算负对数似然损失时需进行复杂运算。现有替代方案虽提出了易于实现的简化损失函数以避免复杂计算，却难以表达对称分布特性。本文提出一种可快速计算且易于实现的宾厄姆分布负对数似然损失函数，同时构建推理网络，证明该损失函数能够从目标对象的点云数据中捕捉其对称性特征。