Abstract
In this paper, we introduce a new technique for change detection in urban environment based on the comparison of 3D point clouds with significantly different density characteristics. Our proposed approach extracts moving objects and environmental changes from sparse and inhomogeneous instant 3D (i3D) measurements, using as reference background model dense and regular point clouds captured by mobile laser scanning (MLS) systems. The introduced workflow consist of consecutive steps of point cloud classification, crossmodal measurement registration, Markov Random Field based change extraction in the range image domain and label back projection to 3D. Experimental evaluation is conducted in four different urban scenes, and the advantage of the proposed change detection step is demonstrated against a reference voxel based approach.
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Acknowledgment
This work was supported by the Hungarian National Research, Development and Innovation Fund (NKFIA #K-120233). C. Benedek also acknowledges the support of the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. MLS test data was provided by the Road Management Department of the City Council of Budapest (Budapest Közút Zrt).
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Gálai, B., Benedek, C. (2017). Change Detection in Urban Streets by a Real Time Lidar Scanner and MLS Reference Data. In: Karray, F., Campilho, A., Cheriet, F. (eds) Image Analysis and Recognition. ICIAR 2017. Lecture Notes in Computer Science(), vol 10317. Springer, Cham. https://doi.org/10.1007/978-3-319-59876-5_24
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DOI: https://doi.org/10.1007/978-3-319-59876-5_24
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