Most recent 6D object pose estimation methods, including unsupervised ones,
require many real training images. Unfortunately, for some applications, such
as those in space or deep under water, acquiring real images, even unannotated,
is virtually impossible. In this paper, we propose a method that can be trained
solely on synthetic images, or optionally using a few additional real ones.
Given a rough pose estimate obtained from a first network, it uses a second
network to predict a dense 2D correspondence field between the image rendered
using the rough pose and the real image and infers the required pose
correction. This approach is much less sensitive to the domain shift between
synthetic and real images than state-of-the-art methods. It performs on par
with methods that require annotated real images for training when not using
any, and outperforms them considerably when using as few as twenty real images.

本文提出了一种基于合成图像或少量真实图像训练的 6D 物体位姿估计方法，使用第一网络获得粗略姿态估计后，使用第二网络预测精细的二维对应场，并推断所需的姿态纠正，该方法在显著减少合成图像与真实图像领域差异的情况下具有与需要标注真实图像训练的方法相当的性能，而在使用 20 张真实图像时，它的性能明显优于它们。