Real-time curve-skeleton extraction of human-scanned point clouds: Application in upright human pose estimation

This paper presents a practical and robust approach for upright human curve-skeleton extraction. Curveskeletons are object descriptors that represent a simplified version of the geometry and topology of a 3-D object. The curve-skeleton of a human-scanned point set enables the approximation of the underlying skeletal structure and thus, to estimate the body configuration (human pose). In contrast to most curve-skeleton extraction methodologies from the literature, we herein propose a real-time curve-skeleton extraction approach that applies to scanned point clouds, independently of the object's complexity and/or the amount of noise within the depth measurements. The experimental results show the ability of the algorithm to extract a centered curve-skeleton within the 3-D object, with the same topology, and with unit thickness. The proposed approach is intended for real world applications and hence, it handles large portions of data missing due to occlusions, acquisition hindrances or registration inaccuracies.