An Adaptive MLS Surface for Reconstruction with Guarantees

Abstract

Recent work have shown that moving least squares (MLS) surfaces can be used effectively to reconstruct surfaces from possibly noisy point cloud data. Several variants of MLS surfaces have been suggested, some of which have been analyzed theoretically for guarantees. These analyses, so far, have assumed uniform sampling density. We propose a new variant of the MLS surface that, for the first time, incorporates local feature sizes in its formulation, and we analyze it for reconstruction guarantees using a non-uniform sampling density. The proposed variant of the MLS surface has several computational advantages over existing MLS methods.