Clustering-based refinement for 3D human body parts segmentation

A common approach to address human body parts segmentation on 3D data involves the use of a 2D segmentation network and 3D projection. Following this approach, several errors could be introduced in the final 3D segmentation output, such as segmentation errors and reprojection errors. Such errors are even more significant when considering very small body parts such as hands. In this paper, we propose a new algorithm that aims to reduce such errors and improve 3D segmentation of human body parts. The algorithm detects noise points and wrong clusters using DBSCAN algorithm, and changes the labels of the points exploiting the shape and position of the clusters. We evaluated the proposed algorithm on the 3DPeople synthetic dataset and on a real dataset, highlighting how it can greatly improve the 3D segmentation of small body parts like hands. With our algorithm we achieved an improvement up to 4.68% of IoU on the synthetic dataset and up to 2.30% of IoU in the real scenario.