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Summary: Dense Depth and Color Acquisition of Repetitive Motions
Yi Xu Daniel G. Aliaga
Department of Computer Science at Purdue University
{xu43|aliaga}@cs.purdue.edu
Abstract
Modeling dynamic scenes is a challenging
problem faced by applications such as digital content
generation and motion analysis. Fast single-frame
methods obtain sparse depth samples while multiple-
frame methods often reply on the rigidity of the object
to correspond a small number of consecutive shots for
decoding the pattern by feature tracking. We present a
novel structured-light acquisition method which can
obtain dense depth and color samples for moving and
deformable surfaces undergoing repetitive motions.
Our key observation is that for repetitive motion,
different views of the same motion state under different
structured-light patterns can be corresponded together
by image matching. These images densely encode an
effectively "static" scene with time-multiplexed
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