Extracting curve-skeletons from digital shapes using occluding contours.

Abstract: Curve-skeletons are compact and semantically relevant shape descriptors, able to summarize both topology and pose of a wide range of digital objects. Most of the state-of-the-art algorithms for their computation rely on the type of geometric primitives used and sampling frequency. In this paper we introduce a formally sound and intuitive definition of curve-skeleton, then we propose a novel method for skeleton extraction that rely on the visual appearance of the shapes. To achieve this result we inspect the properties of occluding contours, showing how information about the symmetry axes of a 3D shape can be inferred by a small set of its planar projections. The proposed method is fast, insensitive to noise, capable of working with different shape representations, resolution insensitive and easy to implement.

Authors: M. Livesu, R. Scateni.
Extracting curve-skeletons from digital shapes using occluding contours.
The Visual Computer, 29(9):907-916. (CGI 2013, Hannover, Germania)
Springer, Giugno 2013.

Rigid registration of different poses of animated shapes

Abstract: Different poses of 3D models are very often given in different positions and orientations in space. Since most of the computer graphics algorithms do not satisfy geometric invariance, it is very important to bring shapes into a canonical coordinate frame before any processing. In this paper we consider the problem of finding the best alignment between two or more different poses of the same object represented by triangle meshes sharing the same connectivity. Firstly, we developed a method to select a region of interest (ROI) which has a perfect alignment over the two poses (up to a rigid movement). Secondary, we solved a simplified version of the Largest Common Point-set (LCP) problem with a-priori knowledge about point correspondence, in order to align the ROIs. We eventually align the poses performing least square rigid registration. Our method makes no assumption about the starting positions of the objects and can also be used with more than two poses at once. It is fast, non-iterative, easy to reproduce and brings the poses into the best alignment whatever the initial positions are.

Authors: M. Livesu, R. Scateni.
Rigid registration of different poses of animated shapes.
Journal of WSCG, 21(1):1-10 (WSCG 2013, Plzen, Rep. Ceca).
University of West Bohemia, Giugno 2013.