Modeling Algebraic Point Set Surfaces using a fixed number of input nearest points results in unacceptable approximations far from densely sampled regions. Our smooth approximation copes naturally with complex inputs featuring large missing parts and competes with global optimization approaches while allowing for pointwise smooth projection and filtering.
Abstract
We present a simple, fast, and smooth scheme to approximate Algebraic Point Set Surfaces using non-compact kernels, which is particularly suited 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. 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.
We demonstrate our technique on a variety of point sets featuring geometric noise as well as large holes.
@article{MLRBT:2022:MLoD,
title = "Moving Level-of-Detail Surfaces",
author = "Corentin Mercier and Thibault Lescoat and Pierre Roussillon and Tamy Boubekeur and Jean-Marc Thiery",
year = "2022",
journal = "ACM Transactions on Graphics (Proc. SIGGRAPH 2022)",
number = "4",
volume = "41",
articleno = "130",
numpages = "10",
doi = "10.1145/3528223.3530151"
}
