Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds

Abstract

A framework for extracting salient local features from 3D models is presented in this paper. In the proposed method, the amount of curvature at a surface point is specified by a positive quantitative measure known as the curvedness. This value is invariant to rigid body transformation such translation and rotation. The curvedness at a surface position is calculated at multiple scales by fitting a manifold to the local neighbourhoods of different sizes. Points corresponding to local maxima and minima of curvedness are selected as suitable features and a confidence measure of each keypoint is also calculated based on the deviation of its curvedness from the neighbouring values. The advantage of this framework is its applicability to both 3D meshes and unstructured point clouds. Experimental results on a different number of models are shown to demonstrate the effectiveness and robustness of our approach.

Segmentation-based Multi-scale Edge Extraction to Measure the Persistence of Features in Unorganized Point Clouds

“…Pauly (Pauly et al, 2003a) proposes a surface variation method based on PCA at multiple scales, and evaluates the likelihood of a point belonging to a feature. Instead of surface variation, Ho (Ho and Gibbines, 2009) adopts the rotation and translation invariant local surface measure as a local geometric property to compute the feature confidence value and suitable scales. In addition, according to the significance of the normals in the point cloud, the authors in (Ioannou et al, 2015) proposed a multi-scale approach by computing the difference of normals.…”

Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

“…A similar approach is given in Steder et al [55]. Ho and Gibbins [56] suggested to compute such features through the scale space, i.e., for neighborhoods of a different size. All these features can be interpreted geometrically.…”

Multi-scale tensor voting for feature extraction from unstructured point clouds

“…Pauly et al [10] propose the surface variation based on PCA at multiple scales, and evaluate the likelihood that a point belongs to a feature. Instead of the surface variation, Ho and Gibbins [18] adopt the rotation and translation invariant local surface measure, the curvedness, as a local geometric property to compute the feature confidence value based on the curvedness at current and adjacent scales. They find an optimal scale for the given model by investigating a local extremum in the geometric measures across the scale axis.…”