  We introduce a framework for the recovery of points on a smooth surface in
high-dimensional space, with application to dynamic imaging. We assume the
surface to be the zero-level set of a bandlimited function. We show that the
exponential maps of the points on the surface satisfy annihilation relations,
implying that they lie in a finite dimensional subspace. We rely on nuclear
norm minimization of the maps to recover the points from noisy and undersampled
measurements. Since this direct approach suffers from the curse of
dimensionality, we introduce an iterative reweighted algorithm that uses the
"kernel trick". The resulting algorithm has similarities to iterative
algorithms used in graph signal processing (GSP); this framework can be seen as
a continuous domain alternative to discrete GSP theory. The use of the
algorithm in recovering free breathing and ungated cardiac data shows the
potential of this framework in practical applications.
