Object pose estimation is a fundamental computer vision task exploited in
several robotics and augmented reality applications. Many established
approaches rely on predicting 2D-3D keypoint correspondences using RANSAC
(Random sample consensus) and estimating the object pose using the PnP
(Perspective-n-Point) algorithm. Being RANSAC non-differentiable,
correspondences cannot be directly learned in an end-to-end fashion. In this
paper, we address the stereo image-based object pose estimation problem by (i)
introducing a differentiable RANSAC layer into a well-known monocular pose
estimation network; (ii) exploiting an uncertainty-driven multi-view PnP solver
which can fuse information from multiple views. We evaluate our approach on a
challenging public stereo object pose estimation dataset, yielding
state-of-the-art results against other recent approaches. Furthermore, in our
ablation study, we show that the differentiable RANSAC layer plays a
significant role in the accuracy of the proposed method. We release with this
paper the open-source implementation of our method.

本文介绍了一种使用可微分 RANSAC 层和基于不确定性的多视图 PnP 求解器对基于立体图像的物体姿态估计问题进行解决的方法，并在公开数据集上进行了评估，取得了与其他最近方法相比的最新结果。