Point cloud features suitable for automatic labeling of MMS point cloud data

For road mapping, it is important to add labels to point clouds captured by the Mobile Mapping System (MMS). Some automatic labeling methods have been proposed so far. However, in our experiment, conventional labeling methods were not sufficiently accurate for actual point clouds measured in Japan....

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Published inShashin sokuryō to rimōto senshingu Vol. 60; no. 5; pp. 266 - 275
Main Authors TAKAHASHI, Genki, MASUDA, Hiroshi
Format Journal Article
LanguageEnglish
Japanese
Published Tokyo Japan Science and Technology Agency 2021
Subjects
Labeling
Labels
Mapping
Online AccessGet full text
ISSN0285-5844
1883-9061
DOI10.4287/jsprs.60.266

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Abstract For road mapping, it is important to add labels to point clouds captured by the Mobile Mapping System (MMS). Some automatic labeling methods have been proposed so far. However, in our experiment, conventional labeling methods were not sufficiently accurate for actual point clouds measured in Japan. In this paper, we propose a high-performance classification method that combines the multi-scale features of point clouds, the MMS specific features and the features obtained from point clouds mapped on the 2D image. The accuracy of the proposed method was evaluated using actual MMS data, and it was confirmed that the proposed method could achieve high recognition rate generalization performance. Our method can improve the accuracy of automatic labeling of point clouds, and is expected to improve the efficiency of map maintenance, which is a social infrastructure.
AbstractList For road mapping, it is important to add labels to point clouds captured by the Mobile Mapping System (MMS). Some automatic labeling methods have been proposed so far. However, in our experiment, conventional labeling methods were not sufficiently accurate for actual point clouds measured in Japan. In this paper, we propose a high-performance classification method that combines the multi-scale features of point clouds, the MMS specific features and the features obtained from point clouds mapped on the 2D image. The accuracy of the proposed method was evaluated using actual MMS data, and it was confirmed that the proposed method could achieve high recognition rate generalization performance. Our method can improve the accuracy of automatic labeling of point clouds, and is expected to improve the efficiency of map maintenance, which is a social infrastructure.
Author TAKAHASHI, Genki
MASUDA, Hiroshi
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Title Point cloud features suitable for automatic labeling of MMS point cloud data
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