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Comparison of belief propagation and graph-cut approaches for contextual classification of 3D lidar point cloud data

Abstract : In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach, we present the classification results derived for a standard benchmark dataset. These results clearly indicate that the graph-cut-based method is able to retrieve a labeling of higher likelihood in only a fraction of the time needed for the other approaches. The higher likelihood, in turn, translates into a significant gain in the accuracy of the obtained classification.
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https://hal.archives-ouvertes.fr/hal-01500777
Contributor : Loic Landrieu <>
Submitted on : Monday, April 3, 2017 - 4:31:20 PM
Last modification on : Tuesday, May 12, 2020 - 8:28:22 AM
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  • HAL Id : hal-01500777, version 1

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Loic Landrieu, Clément Mallet, M Weinmann. Comparison of belief propagation and graph-cut approaches for contextual classification of 3D lidar point cloud data. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Jul 2017, Fort Worth, Texas, United States. ⟨hal-01500777⟩

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