Section:
New Results
Sparse Multi-View Consistency
for Object Segmentation
Multiple view segmentation consists in segmenting objects
simultaneously in several views. A key issue in that respect and
compared to monocular settings is to ensure propagation of
segmentation information between views while minimizing complexity
and computational cost. In this work, we first investigate the idea
that examining measurements at the projections of a sparse set of 3D
points is sufficient to achieve this goal. The proposed algorithm
softly assigns each of these 3D samples to the scene background if
it projects on the background region in at least one view, or to the
foreground if it projects on foreground region in all views. Second,
we show how other modalities such as depth may be seamlessly
integrated in the model and benefit the segmentation. The paper
exposes a detailed set of experiments used to validate the
algorithm, showing results comparable with the state of art, with
reduced computational complexity. We also discuss the use of
different modalities for specific situations, such as dealing with a
low number of viewpoints or a scene with color ambiguities between
foreground and background. This work was published as article in the
PAMI journal [3] .
Figure
5. Three views of the Plant dataset as processed by our
method for mutli-view silhouette extraction [3] .
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