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The Visual Computer

, Volume 35, Issue 4, pp 489–505 | Cite as

Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes

  • Wei ZhouEmail author
  • Caiwen Ma
  • Tong Yao
  • Peng Chang
  • Qi Zhang
  • Arjan Kuijper
Original Article

Abstract

3D feature descriptors provide essential information to find given models in captured scenes. In practical applications, these scenes often contain clutter. This imposes severe challenges on the 3D object recognition leading to feature mismatches between scenes and models. As such errors are not fully addressed by the existing methods, 3D feature matching still remains a largely unsolved problem. We therefore propose our Histograms of Gaussian Normal Distribution (HGND) for capturing salient feature information on a local reference frame (LRF) that enables us to solve this problem. We define a LRF on each local surface patch by using the eigenvectors of the scatter matrix. Different from the traditional local LRF-based methods, our HGND descriptor is based on the combination of geometrical and spatial information without calculating the distribution of every point and its geometrical information in a local domain. This makes it both simple and efficient. We encode the HGND descriptors in a histogram by the geometrical projected distribution of the normal vectors. These vectors are based on the spatial distribution of the points. We use three public benchmarks, the Bologna, the UWA and the Ca’ Foscari Venezia dataset, to evaluate the speed, robustness, and descriptiveness of our approach. Our experiments demonstrate that the HGND is fast and obtains a more reliable matching rate than state-of-the-art approaches in cluttered situations.

Keywords

Local surface patch Local reference frame Local feature descriptor Point cloud 

Notes

Acknowledgements

This work was supported by the University of Chinese Academy of Sciences (UCAS) Joint PhD Training Program (UCAS[2015]37).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Xi’an Institute of Optics and Precision Mechanics of CASXi’anChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Fraunhofer IGDTU DarmstadtDarmstadtGermany
  4. 4.Northeastern UniversityBostonUSA

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