Graph Based Over-Segmentation Methods for 3D Point Clouds
This work addresses the need for better preliminary segmentation in computer vision applications using 3D data, but it is incremental as it builds on existing 2D methods.
The authors tackled the problem of over-segmentation for 3D point clouds by extending graph-based methods to incorporate geometric information, resulting in the Point Cloud Local Variation (PCLV) algorithm that shows significant improvement over state-of-the-art 2D and 3D methods in performance measures.
Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications. New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information. This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points. We consider a variety of possible 3D extensions of the Local Variation (LV) graph based over-segmentation algorithms, and compare them thoroughly. We consider different alternatives for constructing the connectivity graph, for assigning the edge weights, and for defining the merge criterion, which must now account for the geometric information and not only color. Following this evaluation, we derive a new generic algorithm for over-segmentation of 3D point clouds. We call this new algorithm Point Cloud Local Variation (PCLV). The advantages of the new over-segmentation algorithm are demonstrated on both outdoor and cluttered indoor scenes. Performance analysis of the proposed approach compared to state-of-the-art 2D and 3D over-segmentation algorithms shows significant improvement according to the common performance measures.