Abstract
In this paper, we develop an interest point detector and
binary feature descriptor for spherical images. We take as
inspiration a recent framework developed for planar images, BRISK (Binary Robust Invariant Scalable Keypoints),
and adapt the method to operate on spherical images. All of
our processing is intrinsic to the sphere and avoids the distortion inherent in storing and indexing spherical images in
a 2D representation. We discretise images on a spherical
geodesic grid formed by recursive subdivision of a triangular mesh. This leads to a multiscale pixel grid comprising mainly hexagonal pixels that lends itself naturally to a
spherical image pyramid representation. For interest point
detection, we use a variant of the Accelerated Segment Test
(AST) corner detector which operates on our geodesic grid.
We estimate a continuous scale and location for features
and descriptors are built by sampling onto a regular pattern in the tangent space. We evaluate repeatability, precision and recall on both synthetic spherical images with
known ground truth correspondences and real images.