  In this paper we tackle the problem of estimating the 3D pose of object
instances, using convolutional neural networks. State of the art methods
usually solve the challenging problem of regression in angle space indirectly,
focusing on learning discriminative features that are later fed into a separate
architecture for 3D pose estimation. In contrast, we propose an end-to-end
learning framework for directly regressing object poses by exploiting Siamese
Networks. For a given image pair, we enforce a similarity measure between the
representation of the sample images in the feature and pose space respectively,
that is shown to boost regression performance. Furthermore, we argue that our
pose-guided feature learning using our Siamese Regression Network generates
more discriminative features that outperform the state of the art. Last, our
feature learning formulation provides the ability of learning features that can
perform under severe occlusions, demonstrating high performance on our novel
hand-object dataset.
