A novel approach based on cluster-group for classification of 3D residential scene

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 bas...

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Bibliographic Details
Published in2014 International Conference on Information Science, Electronics and Electrical Engineering Vol. 3; pp. 1460 - 1464
Main Authors Guiliang Lu, Yu Zhou, Yao Yu, Sidan Du
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2014
Subjects
3D Semantic Segmentation
Classification
Feature extraction
Image segmentation
Laser radar
Lidar
Markov random fields
Point Cloud
Roads
Robots
Three-dimensional displays
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Summary: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.
ISBN:9781479931965
1479931969
DOI:10.1109/InfoSEEE.2014.6946162