  Unlike in natural images, in endoscopy there is no clear notion of an
up-right camera orientation. Endoscopic videos therefore often contain large
rotational motions, which require keypoint detection and description algorithms
to be robust to these conditions. While most classical methods achieve
rotation-equivariant detection and invariant description by design, many
learning-based approaches learn to be robust only up to a certain degree. At
the same time learning-based methods under moderate rotations often outperform
classical approaches. In order to address this shortcoming, in this paper we
propose RIDE, a learning-based method for rotation-equivariant detection and
invariant description. Following recent advancements in group-equivariant
learning, RIDE models rotation-equivariance implicitly within its architecture.
Trained in a self-supervised manner on a large curation of endoscopic images,
RIDE requires no manual labeling of training data. We test RIDE in the context
of surgical tissue tracking on the SuPeR dataset as well as in the context of
relative pose estimation on a repurposed version of the SCARED dataset. In
addition we perform explicit studies showing its robustness to large rotations.
Our comparison against recent learning-based and classical approaches shows
that RIDE sets a new state-of-the-art performance on matching and relative pose
estimation tasks and scores competitively on surgical tissue tracking.
