  One of the challenging input settings for visual servoing is when the initial
and goal camera views are far apart. Such settings are difficult because the
wide baseline can cause drastic changes in object appearance and cause
occlusions. This paper presents a novel self-supervised visual servoing method
for wide baseline images which does not require 3D ground truth supervision.
Existing approaches that regress absolute camera pose with respect to an object
require 3D ground truth data of the object in the forms of 3D bounding boxes or
meshes. We learn a coherent visual representation by leveraging a geometric
property called 3D equivariance-the representation is transformed in a
predictable way as a function of 3D transformation. To ensure that the
feature-space is faithful to the underlying geodesic space, a geodesic
preserving constraint is applied in conjunction with the equivariance. We
design a Siamese network that can effectively enforce these two geometric
properties without requiring 3D supervision. With the learned model, the
relative transformation can be inferred simply by following the gradient in the
learned space and used as feedback for closed-loop visual servoing. Our method
is evaluated on objects from the YCB dataset, showing meaningful outperformance
on a visual servoing task, or object alignment task with respect to
state-of-the-art approaches that use 3D supervision. Ours yields more than 35%
average distance error reduction and more than 90% success rate with 3cm error
tolerance.
