拟合曲面重构This paper proposes a general framework for overfitting control in surface reconstruction from noisy point data.The problem we deal with is how to create a model that will capture as much detail as possible and simultaneously
avoid reproducing the noise of the input points. The proposed framework is based on extra-sample validation. It
is fully automatic and can work in conjunction with any surface reconstruction algorithm. We test the framework
with a Radial Basis Function algorithm, Multi-level Partition of Unity implicits, and the Power Crust algorithm.
