Abstract
This paper addresses the task of dense non-rigid
structure-from-motion (NRSfM) using multiple images.
State-of-the-art methods to this problem are often hurdled
by scalability, expensive computations, and noisy measurements. Further, recent methods to NRSfM usually either
assume a small number of sparse feature points or ignore
local non-linearities of shape deformations, and thus cannot reliably model complex non-rigid deformations. To address these issues, in this paper, we propose a new approach
for dense NRSfM by modeling the problem on a Grassmann
manifold. Specifically, we assume the complex non-rigid
deformations lie on a union of local linear subspaces both
spatially and temporally. This naturally allows for a compact representation of the complex non-rigid deformation
over frames. We provide experimental results on several
synthetic and real benchmark datasets. The procured results clearly demonstrate that our method, apart from being
scalable and more accurate than state-of-the-art methods,
is also more robust to noise and generalizes to highly nonlinear deformations