BRISKS: Binary Features for Spherical Images on a Geodesic Grid

Hao Guan, William A. P. Smith
2017 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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
more » ... 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.
doi:10.1109/cvpr.2017.519 dblp:conf/cvpr/GuanS17 fatcat:xrbeufvzond6vcye3r6zbrjxye