Reduction of Point Cloud Artifacts Using Shape Priors Estimated with the Gaussian Process Latent Variable Model

  • Jens KrenzinEmail author
  • Olaf HellwichEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)


We present a method that removes point cloud artifacts like noisy points, missing data and outliers from a point cloud using a learned shape prior. The shape prior is learned with the Gaussian Process Latent Variable Model from a set of reference objects. As input data our method uses the estimated object pose from an object detector and a segmented point cloud. We show that the estimated shape prior is capable of modeling fine details to a certain degree. We also show that after applying our method the measured accuracy and completeness is increasing.


Point Cloud Discrete Cosine Transform Object Class Discrete Cosine Transform Coefficient Latent Variable Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer Vision & Remote SensingTU BerlinBerlinGermany

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