  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 and a custom-built
dataset we call Transparent Tableware Dataset (TTD), 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 code of our
method and the TTD dataset.
