We formulate multi-view 3D shape reconstruction as the computation
of a minimum cut on the dual graph of a semiregular, multi-resolution,
tetrahedral mesh. Our method does not assume that the surface lies
within a finite band around the visual hull or any other base surface.
Instead, it uses photo-consistency to guide the adaptive subdivision of
a coarse mesh of the bounding volume. This generates a multi-resolution
volumetric mesh that is densely tesselated in the parts likely to
contain the unknown surface. The graph-cut on the dual graph of this
tetrahedral mesh produces a minimum cut corresponding to a triangulated
surface that minimizes a global surface cost functional. Our method
makes no assumptions about topology and can recover deep concavities
when enough cameras observe them. Our formulation also allows
silhouette constraints to be enforced during the graph-cut step to
counter its inherent bias for producing minimal surfaces. Local shape
refinement via surface deformation is used to recover details in the
reconstructed surface. Reconstructions of the Multi-View Stereo
Evaluation benchmark datasets and other real datasets show the
effectiveness of our method.
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