Highly Parallelizable Algorithm for Keypoint Detection in 3-D Point Clouds

  • Jens GarstkaEmail author
  • Gabriele Peters
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 383)


In computer vision a reliable recognition and classification of objects is an essential milestone on the way to autonomous scene understanding. In particular, keypoint detection is an essential prerequisite towards its successful implementation. The aim of keypoint algorithms is the identification of such areas within 2-D or 3-D representations of objects which have a particularly high saliency and which are as unambiguous as possible. While keypoints are widely used in the 2-D domain, their 3-D counterparts are more rare in practice. One of the reasons often consists in their long computation time. We present a highly parallelizable algorithm for 3-D keypoint detection which can be implemented on modern GPUs for fast execution. In addition to its speed, the algorithm is characterized by a high robustness against rotations and translations of the objects and a moderate robustness against noise. We evaluate our approach in a direct comparison with state-of-the-art keypoint detection algorithms in terms of repeatability and computation time.


3-D keypoint detection 3-D object recognition 3-D computer vision 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer Science, Human-Computer InteractionFernUniversität in Hagen, University of HagenHagenGermany

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