  Compared to 2D object bounding-box labeling, it is very difficult for humans
to annotate 3D object poses, especially when depth images of scenes are
unavailable. This paper investigates whether we can estimate the object poses
effectively when only RGB images and 2D object annotations are given. To this
end, we present a two-step pose estimation framework to attain 6DoF object
poses from 2D object bounding-boxes. In the first step, the framework learns to
segment objects from real and synthetic data in a weakly-supervised fashion,
and the segmentation masks will act as a prior for pose estimation. In the
second step, we design a dual-scale pose estimation network, namely
DSC-PoseNet, to predict object poses by employing a differential renderer. To
be specific, our DSC-PoseNet firstly predicts object poses in the original
image scale by comparing the segmentation masks and the rendered visible object
masks. Then, we resize object regions to a fixed scale to estimate poses once
again. In this fashion, we eliminate large scale variations and focus on
rotation estimation, thus facilitating pose estimation. Moreover, we exploit
the initial pose estimation to generate pseudo ground-truth to train our
DSC-PoseNet in a self-supervised manner. The estimation results in these two
scales are ensembled as our final pose estimation. Extensive experiments on
widely-used benchmarks demonstrate that our method outperforms state-of-the-art
models trained on synthetic data by a large margin and even is on par with
several fully-supervised methods.
