Histogram of distances for local surface description

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dc.contributor.authorKechagias-Stamatis, Odysseas
dc.contributor.authorAouf, Nabil
dc.date.accessioned2017-02-01T10:09:00Z
dc.date.available2017-02-01T10:09:00Z
dc.date.issued2016-06-09
dc.description.abstract3D object recognition is proven superior compared to its 2D counterpart with numerous implementations, making it a current research topic. Local based proposals specifically, although being quite accurate, they limit their performance on the stability of their local reference frame or axis (LRF/A) on which the descriptors are defined. Additionally, extra processing time is demanded to estimate the LRF for each local patch. We propose a 3D descriptor which overrides the necessity of a LRF/A reducing dramatically processing time needed. In addition robustness to high levels of noise and non-uniform subsampling is achieved. Our approach, namely Histogram of Distances is based on multiple L2-norm metrics of local patches providing a simple and fast to compute descriptor suitable for time-critical applications. Evaluation on both high and low quality popular point clouds showed its promising performance.en_UK
dc.identifier.citationKechagias-Stamatis O, Aouf N. Histogram of distances for local surface description. 2016 IEEE International Conference on Robotics and Automation (ICRA) 16-21 May 2016, Stockholm, Swedenen_UK
dc.identifier.urihttps://doi.org/10.1109/ICRA.2016.7487402
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/11379
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.titleHistogram of distances for local surface descriptionen_UK
dc.typeArticleen_UK

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