The method, unveiled during the SIGGRAPH 2022 conference, utilizes non-compact kernels.
During the SIGGRAPH 2022 conference, the Adobe Research team's Corentin Mercier, Thibault Lescoat, Pierre Roussillon, Tamy Boubekeur, and Jean Thiery presented a new method of approximating Algebraic Point Set Surfaces using non-compact kernels. According to the team, the proposed simple and fast approach can be used for filtering and reconstructing point sets presenting large missing parts.
"Our key idea is to consider a moving level-of-detail of the input point set which is adaptive w.r.t. to the evaluation location, just such as the samples weights are output sensitive in the traditional moving least squares scheme," comments the team. "We also introduce an adaptive progressive octree refinement scheme, driven by the resulting implicit surface, to properly capture the modeled geometry even far away from the input samples."
"Similarly to typical compactly-supported approximations, our operator runs in logarithmic time while defining high-quality surfaces even on challenging inputs for which only global optimizations achieve reasonable results," added the researchers.
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