Authors:
Trung-Thien Tran
;
Van-Toan Cao
;
Van Tung Nguyen
;
Sarah Ali
and
Denis Laurendeau
Affiliation:
Laval University, Canada
Keyword(s):
Sharp feature, Feature extraction, Projected distance, Point cloud and mesh processing.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Geometric Computing
;
Geometry and Modeling
;
Modeling and Algorithms
;
Scene and Object Modeling
;
Surface Modeling
Abstract:
A novel algorithm is proposed for extracting sharp features automatically from scanned 3D data of man-made CAD-like objects. The input of our method consists of a mesh or an unstructured point cloud that is captured on the object surface. First, the vector between a given point and the centroid of its neighborhood at a given scale is projected on the normal vector and called the 'projected distance' at this point. This projected distance is calculated for every data point. In a second stage, Otsu's method is applied to the histogram of the projected distances in order to select the optimal threshold value, which is used to detect potential sharp features at a single scale. These two stages are applied iteratively with the other incremental scales. Finally, points recorded as potential features at every scale are marked as valid sharp features. The method has many advantages over existing methods such as intrinsic simplicity, automatic selection of threshold value, accurate and robust
detection of sharp features on various objects. To demonstrate the robustness of the method, it is applied on both synthetic and real 3D data of point clouds and meshes with different noise levels.
(More)