articleMay 1, 2009Closed access

Fast Point Feature Histograms (FPFH) for 3D registration

Technical University of Munich

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Abstract

In our recent work [1], [2], we proposed Point Feature Histograms (PFH) as robust multi-dimensional features which describe the local geometry around a point p for 3D point cloud datasets. In this paper, we modify their mathematical expressions and perform a rigorous analysis on their robustness and complexity for the problem of 3D registration for overlapping point cloud views. More concretely, we present several optimizations that reduce their computation times drastically by either caching previously computed values or by revising their theoretical formulations. The latter results in a new type of local features, called Fast Point Feature Histograms (FPFH), which retain most of the discriminative power of…

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Authors

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Topics & keywords

Keywords
  • Point cloud
  • Robustness (evolution)
  • Discriminative model
  • Histogram
  • Computer science
  • Computation
  • Artificial intelligence
  • Feature (linguistics)
UN Sustainable Development Goals
  • Reduced inequalities
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