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Free keywords:
Computer Science, Computer Vision and Pattern Recognition, cs.CV
Abstract:
We present a novel approach for the reconstruction of dynamic geometric
shapes using a single hand-held consumer-grade RGB-D sensor at real-time rates.
Our method does not require a pre-defined shape template to start with and
builds up the scene model from scratch during the scanning process. Geometry
and motion are parameterized in a unified manner by a volumetric representation
that encodes a distance field of the surface geometry as well as the non-rigid
space deformation. Motion tracking is based on a set of extracted sparse color
features in combination with a dense depth-based constraint formulation. This
enables accurate tracking and drastically reduces drift inherent to standard
model-to-depth alignment. We cast finding the optimal deformation of space as a
non-linear regularized variational optimization problem by enforcing local
smoothness and proximity to the input constraints. The problem is tackled in
real-time at the camera's capture rate using a data-parallel flip-flop
optimization strategy. Our results demonstrate robust tracking even for fast
motion and scenes that lack geometric features.
