Object pose estimation is a core computer vision problem and often an essential component in robotics. Pose estimation is usually approached by seeking the single best estimate of an object's pose, but this approach is ill-suited for tasks involving visual ambiguity. In such cases it is desirable to estimate the uncertainty as a pose distribution to allow downstream tasks to make informed decisions. Pose distributions can have arbitrary complexity which motivates estimating unparameterized distributions, however, until now they have only been used for orientation estimation on SO(3) due to the difficulty in training on and normalizing over SE(3). We propose a novel method for pose distribution estimation on SE(3). We use a hierarchical grid, a pyramid, which enables efficient importance sampling during training and sparse evaluation of the pyramid at inference, allowing real time 6D pose distribution estimation. Our method outperforms state-of-the-art methods on SO(3), and to the best of our knowledge, we provide the first quantitative results on pose distribution estimation on SE(3). Code will be available at spyropose.github.io

翻译：物体姿态估计是计算机视觉领域的核心问题，也是机器人技术中的关键组成部分。传统姿态估计通常致力于寻找物体姿态的最优单一解，但在涉及视觉歧义的任务中，这种方法并不适用。此类情况下，需要将姿态不确定性建模为姿态分布，以便下游任务做出更合理的决策。姿态分布可能具有任意复杂性，这使得非参数化分布的估计具有重要价值。然而，由于在SE(3)上进行训练和归一化的困难，此前非参数化分布仅用于SO(3)上的方向估计。我们提出了一种新颖的SE(3)姿态分布估计方法。该方法采用分层网格——即金字塔结构——能够在训练时实现高效重要性采样，并在推理时对金字塔进行稀疏评估，从而实现实时六维姿态分布估计。我们的方法在SO(3)上优于现有最优方法，且据我们所知，首次在SE(3)上提供了姿态分布估计的定量结果。代码将开源至spyropose.github.io