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Poisson Surface Reconstruction
Michael Kazhdan  , Matthew Bolitho and Hugues Hoppe
Eurographics Symposium on Geometry Processing 2006, 61-70.
We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson
formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and
is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a
hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse
linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional
to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate
reconstruction of surfaces with greater detail than previously achievable.
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