  The Hermite radial basis functions (HRBFs) implicits have been used to
reconstruct surfaces from scattered Hermite data points. In this work, we
propose a closed-form formulation to construct HRBF-based implicits by a
quasi-solution approximating the exact solution. A scheme is developed to
automatically adjust the support sizes of basis functions to hold the error
bound of a quasi-solution. Our method can generate an implicit function from
positions and normals of scattered points without taking any global operation.
Working together with an adaptive sampling algorithm, the HRBF-based implicits
can also reconstruct surfaces from point clouds with non-uniformity and noises.
Robust and efficient reconstruction has been observed in our experimental tests
on real data captured from a variety of scenes.
