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Summary: Abstract
Reconstructing large models from images is a
significant challenge for computer vision, computer
graphics, and related fields. In this paper, we present an
approach for simplifying the reconstruction process by
mathematically eliminating external camera parameters.
This results in less parameters to estimate and in an
overall significantly more robust and accurate
reconstruction. We reformulate the problem in such a
manner as to be able to identify invariants, eliminate
superfluous parameters, and measure the performance
of our formulation under various conditions. We
compare a two-step camera orientation-free method,
where the majority of the points are reconstructed using
a linear equation set, and a camera position-and-
orientation free method, using a degree-two equation
set. Both approaches use a full perspective camera and
are applied to synthetic and real-world datasets.
1. Introduction
The modeling and reconstruction of large 3D models
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