  We present a general paradigm for dynamic 3D reconstruction from multiple
independent and uncontrolled image sources having arbitrary temporal sampling
density and distribution. Our graph-theoretic formulation models the
Spatio-temporal relationships among our observations in terms of the joint
estimation of their 3D geometry and its discrete Laplace operator. Towards this
end, we define a tri-convex optimization framework that leverages the geometric
properties and dependencies found among a Euclideanshape-space and the discrete
Laplace operator describing its local and global topology. We present a
reconstructability analysis, experiments on motion capture data and multi-view
image datasets, as well as explore applications to geometry-based event
segmentation and data association.
