CAS-NET: Cascade attention-based sampling neural network for point cloud simplification

dc.cclicenceCC-BY-NCen
dc.contributor.authorChen, Chen
dc.contributor.authorYuan, Hui
dc.contributor.authorLiu, Hao
dc.contributor.authorHou, Junhui
dc.contributor.authorHamzaoui, Raouf
dc.date.acceptance2023-03-13
dc.date.accessioned2023-03-23T14:49:25Z
dc.date.available2023-03-23T14:49:25Z
dc.date.issued2023-07
dc.description.abstractPoint cloud sampling can reduce storage requirements and computation costs for various vision tasks. Traditional sampling methods, such as farthest point sampling, are not geared towards downstream tasks and may fail on such tasks. In this paper, we propose a cascade attention-based sampling network (CAS-Net), which is end-to-end trainable. Specifically, we propose an attention-based sampling module (ASM) to capture the semantic features and preserve the geometry of the original point cloud. Experimental results on the ModelNet40 dataset show that CAS-Net outperforms state-of-the-art methods in a sampling-based point cloud classification task, while preserving the geometric structure of the sampled point cloud.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNational Natural Science Foundation of Chinaen
dc.identifier.citationChen, C., Yuan, H.. Liu, H., Hou, J. and Hamzaoui, R. (2023) CAS-NET: Cascade attention-based sampling neural network for point cloud simplification, Proc. IEEE ICME 2023, Brisbane, July 2023.en
dc.identifier.doihttps://doi.org/10.1109/icme55011.2023.00341
dc.identifier.urihttps://hdl.handle.net/2086/22641
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid62222110 and 62172259en
dc.publisherIEEEen
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectPoint cloudsen
dc.subjectAttention-based samplingen
dc.titleCAS-NET: Cascade attention-based sampling neural network for point cloud simplificationen
dc.typeConferenceen

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