SHAPE RECONSTRUCTION FROM UNORGANIZED POINTS WITH A DATA-DRIVEN LEVEL SET METHOD SHAPE RECONSTRUCTION FROM UNORGANIZED POINTS WITH A DATA-DRIVEN LEVEL SET METHOD

摘要

We propose a new method for shape reconstruction from noisy and unorganized point data. We represent a shape through its signed distance function and formulate shape reconstruction as a constrained energy minimization problem directly based on the observed point set. The associated energy function includes both the likelihood of the observed data points and a smoothness prior on the reconstructed shape. To solve this optimization problem, an efficient data-driven level set method is developed. Our method is robust to local minima, clutter, and noise. It is also applicable to situations where the data are sparse. The topologi-cal nature of the underlying shape is handled automatically through the level set formalism.