3D CNN based phantom object removing from mobile laser scanning data

  title={3D CNN based phantom object removing from mobile laser scanning data},
  author={Bal{\'a}zs Nagy and Csaba Benedek},
  journal={2017 International Joint Conference on Neural Networks (IJCNN)},
In this paper we introduce a new deep learning based approach to detect and remove phantom objects from point clouds produced by mobile laser scanning (MLS) systems. The phantoms are caused by the presence of scene objects moving concurrently with the MLS platform, and appear as long, sparse but irregular point cloud segments in the measurements. We propose a new 3D CNN framework working on a voxelized column-grid to identify the phantom regions. We quantitatively evaluate the proposed model on… CONTINUE READING


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Showing 1-10 of 10 references

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M. De Deuge, A. Quadros, C. Hung, B. Douillard
Proceedings of Australasian Conference on Robotics and Automation, 2013. • 2013
View 1 Excerpt

3D is here: Point Cloud Library (PCL)

2011 IEEE International Conference on Robotics and Automation • 2011
View 2 Excerpts

Outlier Detection in Laser Scanner Point Clouds

S. Sotoodeh, Soheil. Sotoodeh

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