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

页码：1459 / +

页数：2

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