Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorLuo, Chuanyu
dc.contributor.authorLi, Xiaohan
dc.contributor.authorCheng, Nuo
dc.contributor.authorLi, Pu
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-08-29T09:38:43Z
dc.date.available2022-08-29T09:38:43Z
dc.date.issued2022
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/49388
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectmračno bodůcs
dc.subjectsémantická segmentacecs
dc.subjectkřivky vyplňující prostorcs
dc.subjectkonvoluční neuronové sítěcs
dc.titleMVP-Net: Multiple View Pointwise Semantic Segmentation of Large-Scale Point Cloudsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedSemantic segmentation of 3D point cloud is an essential task for autonomous driving environment perception. The pipeline of most pointwise point cloud semantic segmentation methods includes points sampling, neighbor searching, feature aggregation, and classification. Neighbor searching method like K-nearest neighbors algorithm, KNN, has been widely applied. However, the complexity of KNN is always a bottleneck of efficiency. In this paper, we propose an end-to-end neural architecture, Multiple View Pointwise Net, MVP-Net, to efficiently and directly infer large-scale outdoor point cloud without KNN or any complex pre/postprocessing. Instead, assumption-based space filling curves and multi-rotation of point cloud methods are introduced to point feature aggregation and receptive field expanding. Numerical experiments show that the proposed MVP-Net is 11 times faster than the most efficient pointwise semantic segmentation method RandLA-Net [Qin20a] and achieves the same accuracy on the large-scale benchmark SemanticKITTI dataset.en
dc.subject.translatedpoint clouden
dc.subject.translatedsemantic segmentationen
dc.subject.translatedspace filling curvesen
dc.subject.translatedconvolutional neural networksen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2022.1
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 30, Number 1-2 (2021)

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
A03-full.pdfPlný text2,19 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/49388

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 30 (2022)
  6. Volume 30, Number 1-2 (2021)