【开源方案共享】三维点云快速分割算法

标题：Fast 3D point cloudsegmentation using supervoxels with geometry and color for3D scene understanding

作者：Francesco Verdoja1, Diego Thomas2, Akihiro Sugimoto3

来源：ICME

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本文提出了一种新的三维点云快速分割方法。它从点云的超体素分割开始，即首先对点云进行过分割。然后，它利用一种新的度量方法，同时利用几何和颜色信息来迭代合并超体素，从而实现准确的三维分割，其中保持了分割的层次结构。该算法的计算复杂度与输入的大小成线性关系。对两个公开数据集的实验结果表明，提出的方法优于最新技术。

github:https://github.com/fverdoja/Fast-3D-Pointcloud-Segmentation

依赖的库有：PCL1.8+ Opencv4,并且可以支持ROS，基于传统的分割方法的基础上改进的算法。

主要贡献：

·提出基于传统的分割方法改进的三维点云分割算法，并且在公开的数据集上分割更为准确。

·利用一种新的度量方法，同时利用几何和颜色信息来迭代合并超体素，从而实现准确的三维分割。

论文图集

点云的超体素分割

分割的基本方法就是根据点云之间的凹凸性以及角度

分割结果

英文摘要

Segmentation of 3D colored point clouds is a research field with renewed interest thanks to recent availability of inexpensive consumer RGB-D cameras and its importance as an unavoidable low-level step in many robotic applications. However, 3D data’s nature makes the task challenging and, thus, many different techniques are being proposed, all of which require expensive computational costs. This paper presents a novel fast method for 3D colored point cloud segmentation. It starts with supervoxel partitioning of the cloud, i.e., an oversegmentation of the points in the cloud. Then it leverages on a novel metric exploiting both geometry and color to iteratively merge the supervoxels to obtain a 3D segmentation where the hierarchical structure of partitions is maintained. The algorithm also presents computational complexity linear to the size of the input. Experimental results over two publicly available datasets demonstrate that our proposed method outperforms state-of-the-art techniques.