Graph-based denoising for time-varying point clouds

Noisy 3D point clouds arise in many applications. They may be due to errors when creating a 3D model from images or simply to imprecise depth sensors. Point clouds can be given geometrical structure using graphs created from the similarity information between points. This paper introduces a method that uses this graph structure and convex optimization methods to denoise 3D point clouds. A short discussion presents how those methods naturally generalize to time-varying inputs such as 3D point cloud time series.


Presented at:
3DTV Conference 2015, Lisbon, Portugal, July 8-10, 2015
Year:
2015
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 Record created 2015-06-11, last modified 2018-03-17

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