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Abstract:
In this paper, we consider the problem of animation reconstruction, i.e., the
reconstruction of shape and motion of a deformable object from dynamic 3D
scanner data, without using user provided template models.
Unlike previous work that addressed this problem, we do not rely on locally
convergent optimization but present a system that can handle fast motion,
temporally disrupted input, and can correctly match objects that disappear for
extended time periods in acquisition holes due to occlusion.
Our approach is motivated by cartography: We first estimate a few landmark
correspondences, which are extended to a dense matching and then used to
reconstruct geometry and motion. We propose a number of algorithmic building
blocks: a scheme for tracking landmarks in temporally coherent and incoherent
data, an algorithm for robust estimation of dense correspondences under
topological noise, and the integration of local matching techniques to refine
the result.
We describe and evaluate the individual components and propose a complete
animation reconstruction pipeline based on these ideas.
We evaluate our method on a number of standard benchmark data sets
and show that we can obtain correct reconstructions in situations where
other techniques fail completely or require additional user guidance such as a
template model.
