Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction
Author(s)
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
Large-scale 3D reconstruction has recently received
much attention from the computer vision community. Bundle
adjustment is a key component of 3D reconstruction
problems. However, traditional bundle adjustment algorithms
require a considerable amount of memory and computational
resources. In this paper, we present an extremely
efficient, inherently out-of-core bundle adjustment
algorithm. We decouple the original problem into several
submaps that have their own local coordinate systems and
can be optimized in parallel. A key contribution to our
algorithm is making as much progress towards optimizing
the global non-linear cost function as possible using the
fragments of the reconstruction that are currently in core
memory. This allows us to converge with very few global
sweeps (often only two) through the entire reconstruction.
We present experimental results on large-scale 3D reconstruction
datasets, both synthetic and real.
Sponsor
Date
2007-10
Extent
Resource Type
Text
Resource Subtype
Post-print
Proceedings
Proceedings