  Estimating the pose of a camera with respect to a 3D reconstruction or scene
representation is a crucial step for many mixed reality and robotics
applications. Given the vast amount of available data nowadays, many
applications constrain storage and/or bandwidth to work efficiently. To satisfy
these constraints, many applications compress a scene representation by
reducing its number of 3D points. While state-of-the-art methods use
$K$-cover-based algorithms to compress a scene, they are slow and hard to tune.
To enhance speed and facilitate parameter tuning, this work introduces a novel
approach that compresses a scene representation by means of a constrained
quadratic program (QP). Because this QP resembles a one-class support vector
machine, we derive a variant of the sequential minimal optimization to solve
it. Our approach uses the points corresponding to the support vectors as the
subset of points to represent a scene. We also present an efficient
initialization method that allows our method to converge quickly. Our
experiments on publicly available datasets show that our approach compresses a
scene representation quickly while delivering accurate pose estimates.

Computer Vision and Pattern Recognition