Most recent 6D pose estimation frameworks first rely on a deep network to
establish correspondences between 3D object keypoints and 2D image locations
and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm.
This two-stage process, however, is suboptimal: First, it is not end-to-end
trainable. Second, training the deep network relies on a surrogate loss that
does not directly reflect the final 6D pose estimation task.
In this work, we introduce a deep architecture that directly regresses 6D
poses from correspondences. It takes as input a group of candidate
correspondences for each 3D keypoint and accounts for the fact that the order
of the correspondences within each group is irrelevant, while the order of the
groups, that is, of the 3D keypoints, is fixed. Our architecture is generic and
can thus be exploited in conjunction with existing correspondence-extraction
networks so as to yield single-stage 6D pose estimation frameworks. Our
experiments demonstrate that these single-stage frameworks consistently
outperform their two-stage counterparts in terms of both accuracy and speed.

本文介绍了一种基于深度学习的 6D 姿态估计架构，能够直接从对应点回归 6D 姿态，并提出了单阶段 6D 姿态估计框架，能够在准确性和速度方面显著优于现有的双阶段框架。