NDLI: Semantic Parsing of Street Scene Images Using 3D LiDAR Point Cloud

Content Provider	IEEE Xplore Digital Library
Author	Babahajiani, P. Lixin Fan Gabbouj, M.
Copyright Year	2013
Description	Author affiliation: Tampere Univ. of Technol., Tampere, Finland (Babahajiani, P.; Gabbouj, M.) \|\| Nokia Res. Center, Tampere, Finland (Lixin Fan)
Abstract	In this paper we propose a novel street scene semantic parsing framework, which takes advantage of 3D point clouds captured by a high-definition LiDAR laser scanner. Local 3D geometrical features extracted from subsets of point clouds are classified by trained boosted decision trees and then corresponding image segments are labeled with semantic classes e.g. buildings, road, sky etc. In contrast to existing image-based scene parsing approaches, the proposed 3D LiDAR point cloud based approach is robust to varying imaging conditions such as lighting and urban structures. The proposed method is evaluated both quantitatively and qualitatively on three challenging NAVTEQ True databases and robust scene parsing results are reported.
Starting Page	714
Ending Page	721
File Size	1148896
Page Count	8
File Format	PDF
ISBN	9781479930227
DOI	10.1109/ICCVW.2013.98
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-02
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Cloud point Three-dimensional displays Laser radar Accuracy Clouds Image processing Parsing scene Classification Lidar Cameras Feature extraction
Content Type	Text
Resource Type	Article

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