Shape classification and normal estimation for non-uniformly sampled, noisy point data

We present an algorithm for robustly analyzing point data arising from sampling a 2D surface embedded in 3D, even in the presence of noise and nonuniform sampling. The algorithm outputs, for each data point, a surface normal, a local surface approximation in the form of a one-ring, the local shape (flat, ridge, bowl, saddle, sharp edge, corner, boundary), the feature size, and a confidence value that can be used to determine areas where the sampling is poor or not surface-like. We show that the normal estimation out-performs traditional fitting approaches, especially when the data points are non-uniformly sampled and in areas of high curvature. We demonstrate surface reconstruction, parameterization, and smoothing using the one-ring neighborhood at each point as an approximation of the full mesh structure.