# Graph Based Over-Segmentation Methods for 3D Point Clouds

## TL;DR

This paper introduces a novel 3D over-segmentation algorithm called PCLV that leverages both color and geometric information in point clouds, significantly improving segmentation quality over existing methods.

## Contribution

The paper develops a new generic over-segmentation algorithm for 3D point clouds that incorporates geometric data, outperforming current state-of-the-art techniques.

## Key findings

- PCLV outperforms existing algorithms on outdoor and indoor scenes.
- Incorporating geometric information improves segmentation accuracy.
- The method shows significant performance gains over 2D and 3D benchmarks.

## Abstract

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.