论文标题
Ki-pode：基于按键的隐式姿势姿势分布估算刚性对象
Ki-Pode: Keypoint-based Implicit Pose Distribution Estimation of Rigid Objects
论文作者
论文摘要
刚性对象的6D姿势的估计是计算机视觉中的一个基本问题。传统上，姿势估计与确定单一最佳估计有关。但是，单个估计值无法表达视觉歧义，在许多情况下，由于对象对称或识别特征的遮挡，这在许多情况下是不可避免的。无法说明姿势的歧义可能会导致随后的方法失败，这是在失败成本高时无法接受的。与单个估计相反，全姿势分布的估计值非常适合表达姿势的不确定性。在此激励的情况下，我们提出了一种新颖的姿势分布估计方法。对象姿势上概率分布的隐式公式来自对象的中间表示作为一组关键点。这样可以确保姿势分布估计具有很高的解释性。此外，我们的方法基于保守近似，这导致可靠的估计。该方法已被评估在YCB-V和T-less数据集上的旋转分布估计的任务上，并在所有对象上可靠地执行。
The estimation of 6D poses of rigid objects is a fundamental problem in computer vision. Traditionally pose estimation is concerned with the determination of a single best estimate. However, a single estimate is unable to express visual ambiguity, which in many cases is unavoidable due to object symmetries or occlusion of identifying features. Inability to account for ambiguities in pose can lead to failure in subsequent methods, which is unacceptable when the cost of failure is high. Estimates of full pose distributions are, contrary to single estimates, well suited for expressing uncertainty on pose. Motivated by this, we propose a novel pose distribution estimation method. An implicit formulation of the probability distribution over object pose is derived from an intermediary representation of an object as a set of keypoints. This ensures that the pose distribution estimates have a high level of interpretability. Furthermore, our method is based on conservative approximations, which leads to reliable estimates. The method has been evaluated on the task of rotation distribution estimation on the YCB-V and T-LESS datasets and performs reliably on all objects.
