Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A clever elimination strategy for efficient minimal solvers   We present a new insight into the systematic generation of minimal solvers in
computer vision, which leads to smaller and faster solvers. Many minimal
problem formulations are coupled sets of linear and polynomial equations where
image measurements enter the linear equations only. We show that it is useful
to solve such systems by first eliminating all the unknowns that do not appear
in the linear equations and then extending solutions to the rest of unknowns.
This can be generalized to fully non-linear systems by linearization via
lifting. We demonstrate that this approach leads to more efficient solvers in
three problems of partially calibrated relative camera pose computation with
unknown focal length and/or radial distortion. Our approach also generates new
interesting constraints on the fundamental matrices of partially calibrated
cameras, which were not known before.

23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an