Authors:
Jacek Kustra
1
;
Andrei Jalba
2
and
Alexandru Telea
3
Affiliations:
1
University of Groningen and Philips Research, Netherlands
;
2
Eindhoven University of Technology, Netherlands
;
3
University of Groningen, Netherlands
Keyword(s):
Curve Skeletons, Stereo Vision, Shape Reconstruction, GPU Image Processing.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Geometry and Modeling
;
Image and Video Analysis
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Image-Based Modeling
;
Pattern Recognition
;
Shape Representation and Matching
;
Software Engineering
Abstract:
Computing curve skeletons of 3D shapes is a challenging task. Recently, a high-potential technique for this task was proposed, based on integrating medial information obtained from several 2D projections of a 3D shape (Livesu et al., 2012). However effective, this technique is strongly influenced in terms of complexity by the quality of a so-called skeleton probability volume, which encodes potential 3D curve-skeleton locations. In this paper, we extend the above method to deliver a highly accurate and discriminative curve-skeleton probability volume. For this, we analyze the error sources of the original technique, and propose improvements in terms of accuracy, culling false positives, and speed. We show that our technique can deliver point-cloud curve-skeletons which are close to the desired locations, even in the absence of complex postprocessing. We demonstrate our technique on several 3D models.