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Classification of Point Clouds with Neural Networks and Continuum-Type Memories

Abstract : This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully generated point clouds can be used, but knowledge can be extracted from the raw impulses of the respective time points. Attractors consisting of a continuum of stationary states and hysteretic memories can be used to couple multiple inputs over time given non-independent output quantities of a classifier and applied to suitable neural networks. In this paper, we show a way to assign input point clouds to sets of classes using hysteretic memories, which are transferable to neural networks.
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https://hal.inria.fr/hal-03287683
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Submitted on : Thursday, July 15, 2021 - 6:10:53 PM
Last modification on : Friday, August 13, 2021 - 4:29:53 PM
Long-term archiving on: : Saturday, October 16, 2021 - 7:06:40 PM

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Stefan Reitmann, Elena V. Kudryashova, Bernhard Jung, Volker Reitmann. Classification of Point Clouds with Neural Networks and Continuum-Type Memories. 17th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2021, Hersonissos, Crete, Greece. pp.505-517, ⟨10.1007/978-3-030-79150-6_40⟩. ⟨hal-03287683⟩

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