A hybrid clustering pipeline for mining baseline local patterns in 3d point cloud

Rahim Hossain, Manar Samad

Abstract: Three-dimensional (3D) imaging provides detailed geometry of real-world objects, unlike 2D image texture. The rudimentary form of 3D imaging is point clouds that are distinctly different from image pixels in terms of structure and processing methods. The 3D computer vision literature primarily retrieves global shape patterns in 3D data for object and face recognition tasks. In contrast, mining local deformation patterns in 3D data that are independent of global shape is a nontrivial task. This paper proposes a computational pipeline for mining baseline local patterns in 3D point clouds and identifies informative segments of point clouds for data selection and interpretation. We investigate the performance of several clustering algorithms in 3D point cloud segmentation and propose a computationally fast multi-stage clustering pipeline with parametric modeling of local patterns in point clouds. The proposed pipeline has achieved an area under the ROC curve of 0.72 in classifying seven emotional expressions (including the neutral expression) using 3D human facial point clouds. Our results demonstrate the baseline efficacy of raw 3D point coordinates in mining local patterns without involving feature engineering or deep learning. Therefore, the proposed pipeline can serve as a baseline for 1) rapid mining of informative local patterns and 2) selecting important segments of 3D point cloud data. The source code is made publicly available to promote future work in this area.