Robust smooth feature extraction from point clouds

Defining sharp features in a given 3D model facilitates a better understanding of the surface and aids visualizations, reverse engineering, filtering, simplification, non-photo realism, reconstruction and other geometric processing applications. We present a robust method that identifies sharp features in a point cloud by returning a set of smooth curves aligned along the edges. Our feature extraction is a multi-step refinement method that leverages the concept of Robust Moving Least Squares to locally fit surfaces to potential features. Using Newton's method, we project points to the intersections of multiple surfaces then grow polylines through the projected cloud. After resolving gaps, connecting corners, and relaxing the results, the algorithm returns a set of complete and smooth curves that define the features. We demonstrate the benefits of our method with two applications: surface meshing and point-based geometry compression

Spline-based feature curves from point-sampled geometry

Defining sharp features in a 3D model facilitates a better understanding of the surface and aids geometric processing and graphics applications, such as reconstruction, filtering, simplification, reverse engineering, visualization, and non-photo realism. We present a robust method that identifies sharp features in a point-based model by returning a set of smooth spline curves aligned along the edges. Our feature extraction leverages the concepts of robust moving least squares to locally project points to potential features. The algorithm processes these points to construct arc-length parameterized spline curves fit using an iterative refinement method, aligning smooth and continuous curves through the feature points. We demonstrate the benefits of our method with three applications: surface segmentation, surface meshing and point-based compression

ON THINNING METHODS FOR DATA ASSIMILATION OF SATELLITE OBSERVATIONS

Thinning of observational data sets is an essential task in assimilation of satellite data for numerical weather forecast. In this work we modify and improve the scheme of so-called estimation error analysis (EEA). EEA is an adaptive thinning method that iteratively removes observations from a given data set, guided by a special approximation error measure evaluated at all original observation points. We propose EEA variants that differ in methodological and performance aspects, such as the Grid-EEA method, where errors are evaluated on a regular grid on the globe. Moreover, in the Top-Down EEA, we propose to construct the thinnings by an iterative point insertion strategy, which leads to improved performance since the number of insertion steps is typically much smaller than the number of corresponding removal operations in EEA. We also provide an efficient implementation of the proposed methods yielding a significant acceleration of the standard EEA approach