3D Objects Face Clustering using Unsupervised Mean Shift

In this paper, a novel approach to face clustering is proposed. The aim is the extraction of planes of a mesh acquired from a 3D reconstruction process. In this context, as 3D coordinates points are inevitably affected by error, robustness is the main focus. The method is based on mean shift clustering paradigm, devoted to separate the modes of a multimodal density by using a kernel-based technique. A critic parameter, the kernel bandwidth, is automatically detected. Experimental results on synthetic and real data validate the approach and prove its robustness.