A Novel Approach Based on Cluster-group for Classification of 3D Residential Scene

被引：0
作者：
Lu, Guiliang [1 ]
Zhou, Yu [1 ]
Yu, Yao [1 ]
Du, Sidan [1 ]
机构：
[1] Nanjing Univ, Sch Elect Sci & Engn, Nanjing, Peoples R China
来源：
2014 INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE, ELECTRONICS AND ELECTRICAL ENGINEERING (ISEEE), VOLS 1-3 | 2014年
关键词：
3D Semantic Segmentation; Classification; Point Cloud; Lidar; OBJECT RECOGNITION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To understand scenes and help autonomous robots and cars, researchers' attention is directed through the problem of classifying 3D point cloud. In this paper, we present a novel approach to semantically segment 3D point cloud of residential scenes captured by a lidar sensor. Our approach is based on a dual-scale analysis: a small-scale clustering and a large-scale grouping. Features used to train our AdaBoost classifier are then extracted from clusters and groups. We evaluate our method with a challenging lidar data set. The result shows our approach can classify scene objects accurately.
引用
收藏
页码：1459 / +
页数：2
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