General techniques for approximate incidences and their application to the camera posing problem
Abstract
We consider the classical camera pose estimation problem that arises in many computer vision
applications, in which we are given n 2D-3D correspondences between points in the scene and
points in the camera image (some of which are incorrect associations), and where we aim to
determine the camera pose (the position and orientation of the camera in the scene) from this
data. We demonstrate that this posing problem can be reduced to the problem of computing
ε-approximate incidences between two-dimensional surfaces (derived from the input correspondences) and points (on a grid) in a four-dimensional pose space. Similar reductions can be applied
to other camera pose problems, as well as to similar problems in related application areas.
We describe and analyze three techniques for solving the resulting ε-approximate incidences
problem in the context of our camera posing application. The first is a straightforward assignment
of surfaces to the cells of a grid (of side-length ε) that they intersect. The second is a variant
of a primal-dual technique, recently introduced by a subset of the authors [2] for different (and
simpler) applications. The third is a non-trivial generalization of a data structure Fonseca and
Mount [3], originally designed for the case of hyperplanes. We present and analyze this technique
in full generality, and then apply it to the camera posing problem at hand.
We compare our methods experimentally on real and synthetic data. Our experiments show
that for the typical values of n and ε, the primal-dual method is the fastest, also in practice.
Research Areas
