151 research outputs found
Two-View Matching with View Synthesis Revisited
Wide-baseline matching focussing on problems with extreme viewpoint change is
considered. We introduce the use of view synthesis with affine-covariant
detectors to solve such problems and show that matching with the Hessian-Affine
or MSER detectors outperforms the state-of-the-art ASIFT.
To minimise the loss of speed caused by view synthesis, we propose the
Matching On Demand with view Synthesis algorithm (MODS) that uses progressively
more synthesized images and more (time-consuming) detectors until reliable
estimation of geometry is possible. We show experimentally that the MODS
algorithm solves problems beyond the state-of-the-art and yet is comparable in
speed to standard wide-baseline matchers on simpler problems.
Minor contributions include an improved method for tentative correspondence
selection, applicable both with and without view synthesis and a view synthesis
setup greatly improving MSER robustness to blur and scale change that increase
its running time by 10% only.Comment: 25 pages, 14 figure
WxBS: Wide Baseline Stereo Generalizations
We have presented a new problem -- the wide multiple baseline stereo (WxBS)
-- which considers matching of images that simultaneously differ in more than
one image acquisition factor such as viewpoint, illumination, sensor type or
where object appearance changes significantly, e.g. over time. A new dataset
with the ground truth for evaluation of matching algorithms has been introduced
and will be made public.
We have extensively tested a large set of popular and recent detectors and
descriptors and show than the combination of RootSIFT and HalfRootSIFT as
descriptors with MSER and Hessian-Affine detectors works best for many
different nuisance factors. We show that simple adaptive thresholding improves
Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them
on infrared and low contrast images.
A novel matching algorithm for addressing the WxBS problem has been
introduced. We have shown experimentally that the WxBS-M matcher dominantes the
state-of-the-art methods both on both the new and existing datasets.Comment: Descriptor and detector evaluation expande
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