Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds
Patch-Based Semantic Labeling of Road Scene Using Colorized Mobile LiDAR Point Clouds
复制标题
使用彩色移动激光雷达点云对道路场景进行基于补丁的语义标记
DOI：
10.1109/tits.2015.2499196
复制
发表时间：
2016
影响因子：
8.5
通讯作者：
Li Jonathan
中科院分区：
文献类型：
--
作者：
Luo Huan;Wang Cheng;Wen Chenglu;Cai Zhipeng;Chen Ziyi;Wang Hanyun;Yu Yongtao;Li Jonathan
使用彩色移动的LiDAR点云对道路场景进行语义标注在各种应用中，特别是在智能交通系统中具有重要意义。然而，由于遮挡导致的对象不完整性、相邻对象之间的重叠、类间局部相似性以及大量数据点带来的计算负担等问题，使得该领域的研究一直处于开放状态。在本文中，我们提出了一种新的基于块标记彩色移动的激光雷达点云的道路场景的框架。该框架首先利用从点云中提取的3-D面片构造基于3-D面片的匹配图结构（3D-PMG），有效地将标记道路场景的类别标签转移到未标记道路场景。然后，纠正所造成的局部补丁在不同类别的传输错误，三维补丁之间的上下文信息，结合3D-PMG马尔可夫随机场。在实验中，所提出的框架进行了验证的彩色移动的激光雷达点云采集的RIEGL VMX-450移动的激光雷达系统。对比实验表明，该框架的上级性能的道路场景的准确语义标注。
Semantic labeling of road scenes using colorized mobile LiDAR point clouds is of great significance in a variety of applications, particularly intelligent transportation systems. However, many challenges, such as incompleteness of objects caused by occlusion, overlapping between neighboring objects, interclass local similarities, and computational burden brought by a huge number of points, make it an ongoing open research area. In this paper, we propose a novel patch-based framework for labeling road scenes of colorized mobile LiDAR point clouds. In the proposed framework, first, three-dimensional (3-D) patches extracted from point clouds are used to construct a 3-D patch-based match graph structure (3D-PMG), which transfers category labels from labeled to unlabeled point cloud road scenes efficiently. Then, to rectify the transferring errors caused by local patch similarities in different categories, contextual information among 3-D patches is exploited by combining 3D-PMG with Markov random fields. In the experiments, the proposed framework is validated on colorized mobile LiDAR point clouds acquired by the RIEGL VMX-450 mobile LiDAR system. Comparative experiments show the superior performance of the proposed framework for accurate semantic labeling of road scenes.