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3D Point Cloud Analysis

Where can I find certain meaningful structures in my 3D point cloud?

Sven Sickert, and Joachim Denzler


Moment Invariants and Iterative Context Integration

 

[Sickert17_SSO]


Sven Sickert and Joachim Denzler. Semantic Segmentation of Outdoor Areas using 3D Moment Invariants and Contextual Cues. German Conference on Pattern Recognition (GCPR), 2017.  [bib]
3D point cloud of an urban scene
  In this paper, we propose an approach for the semantic segmentation of a 3D point cloud using local 3D moment invariants and the integration of contextual information. Specifically, we focus on the task of analyzing forestal and urban areas which were recorded by terrestrial LiDAR  scanners. We demonstrate how 3D moment invariants can be leveraged as local features and that they are on a par with established descriptors. Furthermore, we show how an iterative learning scheme can increase the overall quality by taking neighborhood relationships between classes into account. Our experiments show that the approach achieves very good results for a variety of tasks including both binary and multi-class settings.