Poisson surface reconstruction with envelope constraints
Symposium on Geometry Processing 2020.
Improved reconstruction using Dirichlet constraints on visual hull or depth hull.
Reconstructing surfaces from scanned 3D points has been an important research area for several decades.
One common approach that has proven efficient and robust to noise is implicit surface reconstruction,
i.e. fitting to the points a 3D scalar function (such as an indicator function or signed-distance field)
and then extracting an isosurface. Though many techniques fall within this category, existing methods
either impose no boundary constraints or impose Dirichlet/Neumann conditions on the surface of a bounding
box containing the scanned data.
In this work, we demonstrate the benefit of supporting Dirichlet constraints on a general boundary. To
this end, we adapt the Screened Poisson Reconstruction algorithm to input a constraint envelope in addition
to the oriented point cloud. We impose Dirichlet boundary conditions, forcing the reconstructed implicit
function to be zero outside this constraint surface. Using a visual hull and/or depth hull derived from
RGB-D scans to define the constraint envelope, we obtain substantially improved surface reconstructions in
regions of missing data.
No hindsights yet.