Extracting 3D mesh skeletons using antipodal points locations

Finding the skeleton of a 3D mesh is an essential task for many applications such as mesh animation, tracking, and 3D registeration. In recent years, new technologies in computer vision such as Microsoft Kinect have proven that a mesh skeleton can be useful such as in the case of human machine interactions. To calculate the 3D mesh skeleton, the mesh properties such as topology and its components relations are utilized. In this paper, we propose the usage of a novel algorithm that can efficiently calculate a vertex antipodal point. A vertex antipodal point is the diametrically opposite point that belongs to the same mesh. The set of centers of the connecting lines between each vertex and its antipodal point represents the 3D mesh desired skeleton. Post processing is completed for smoothing and fitting centers into optimized skeleton parts. The algorithm is tested on different classes of 3D objects and produced efficient results that are comparable with the literature. The algorithm has the advantages of producing high quality skeletons as it preserves details. This is suitable for applications where the mesh skeleton mapping is required to be kept as much as possible.

Towards a parameterless 3D mesh segmentation

This paper proposes a novel technique for 3D mesh segmentation using multiple 2D pose footprints. Such problem has been targeted many times in the literature, but still requires further development especially in the area of automation. The proposed algorithm applies cognition theory and provides a generic technique to form a 3D bounding contour from a seed vertex on the 3D mesh. Forming the cutlines is done in both 2D and 3D spaces to enrich the available information for the search processes. The main advantage of this technique is the possibility to operate without any object-dependent parameters. The parameters that can be used will only be related to the used cognition theory and the seeds suggestion, which is another advantage as the algorithm can be generic to more than one theory of segmentation or to different criterion. The results are competitive against other algorithms, which use object-dependent or tuning parameters. This plus the autonomy and generality features, provides an efficient and usable approach for segmenting 3D meshes and at the same time to reduce the computation load.