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Semantic Labeling and Refinement of LIDAR Point Clouds using Deep Neural Network in Urban Areas

Huang, Rong and Ye, Zheng-Yin and Hong, Danfeng and Xu, Yusheng and Stilla, Uwe (2019) Semantic Labeling and Refinement of LIDAR Point Clouds using Deep Neural Network in Urban Areas. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV2/W7, pp. 63-70. PIA Photogrammetric Image Analysis, 18.-20.10.2019, München. DOI: 10.5194/isprs-annals-IV-2-W7-63-2019 ISSN 2194-9042

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Official URL: https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2-W7/63/2019/

Abstract

In this paper, we propose a framework for obtaining semantic labels of LiDAR point clouds and refining the classification results by combining a deep neural network with a graph-structured smoothing technique. In general, the goal of the semantic scene analysis is to assign a semantic label to each point in the point cloud. Although various related researches have been reported, due to the complexity of urban areas, the semantic labeling of point clouds in urban areas is still a challenging task. In this paper, we address the issues of how to effectively extract features from each point and its local surrounding and how to refine the initial soft labels by considering contextual information in the spatial domain. Specifically, we improve the effectiveness of classification of point cloud in two aspects. Firstly, instead of utilizing handcrafted features as input for classification and refinement, the local context of a point is embedded into deep dimensional space and classified via a deep neural network (PointNet++), and simultaneously soft labels are obtained as initial results for next refinement. Secondly, the initial label probability set is improved by taking the context both in the spatial domain into consideration by constructing a graph structure, and the final labels are optimized by a graph cuts algorithm. To evaluate the performance of our proposed framework, experiments are conducted on a mobile laser scanning (MLS) point cloud dataset. We demonstrate that our approach can achieve higher accuracy in comparison to several commonly-used state-of-the-art baselines. The overall accuracy of our proposed method on TUM dataset can reach 85.38% for labeling eight semantic classes.

Item URL in elib:https://elib.dlr.de/134397/
Document Type:Conference or Workshop Item (Speech)
Title:Semantic Labeling and Refinement of LIDAR Point Clouds using Deep Neural Network in Urban Areas
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Huang, Rongrong.huang (at) tum.deUNSPECIFIED
Ye, Zheng-YinUNSPECIFIEDUNSPECIFIED
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Xu, YushengTUMUNSPECIFIED
Stilla, UwePhotogrammetry and Remote Sensing, Technische Universitaet Muenchen (TUM), Munich, GermanyUNSPECIFIED
Date:October 2019
Journal or Publication Title:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:IV2/W7
DOI :10.5194/isprs-annals-IV-2-W7-63-2019
Page Range:pp. 63-70
Series Name:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
ISSN:2194-9042
Status:Published
Keywords:point clouds, MLS, semantic labeling, deep learning, optimization
Event Title:PIA Photogrammetric Image Analysis
Event Location:München
Event Type:international Conference
Event Dates:18.-20.10.2019
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:11 Mar 2020 09:38
Last Modified:11 Mar 2020 09:38

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