  Image-based object pose estimation sounds amazing because in real
applications the shape of object is oftentimes not available or not easy to
take like photos. Although it is an advantage to some extent, un-explored shape
information in 3D vision learning problem looks like "flaws in jade". In this
paper, we deal with the problem in a reasonable new setting, namely 3D shape is
exploited in the training process, and the testing is still purely image-based.
We enhance the performance of image-based methods for category-agnostic object
pose estimation by exploiting 3D knowledge learned by a multi-modal method.
Specifically, we propose a novel contrastive knowledge distillation framework
that effectively transfers 3D-augmented image representation from a multi-modal
model to an image-based model. We integrate contrastive learning into the
two-stage training procedure of knowledge distillation, which formulates an
advanced solution to combine these two approaches for cross-modal tasks. We
experimentally report state-of-the-art results compared with existing
category-agnostic image-based methods by a large margin (up to +5% improvement
on ObjectNet3D dataset), demonstrating the effectiveness of our method.
