A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf
.
BRISKS: Binary Features for Spherical Images on a Geodesic Grid
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
doi:10.1109/cvpr.2017.519
dblp:conf/cvpr/GuanS17
fatcat:xrbeufvzond6vcye3r6zbrjxye