Zobrazit v EDS

L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions

Uloženo v:
Podrobná bibliografie
Název: L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions
Autoři: Yuxiao Zhang, Ming Ding, Hanting Yang, Yingjie Niu, Yan Feng, Kento Ohtani, Kazuya Takeda
Zdroj: Sensors, Vol 23, Iss 21, p 8660 (2023)
Informace o vydavateli: MDPI AG
Rok vydání: 2023
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: LiDAR point cloud processing, snow noise removal, snow effect generation, CycleGAN, Chemical technology, TP1-1185
Popis: LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the ‘L-DIG’ (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://www.mdpi.com/1424-8220/23/21/8660; https://doaj.org/toc/1424-8220; https://doaj.org/article/edee217b7c614c6193fc25be77b62309
DOI: 10.3390/s23218660
Dostupnost: https://doi.org/10.3390/s23218660
https://doaj.org/article/edee217b7c614c6193fc25be77b62309
Přístupové číslo: edsbas.48773CD5
Databáze: BASE
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://doi.org/10.3390/s23218660#
    Name: EDS - BASE (s4221598)
    Category: fullText
    Text: View record from BASE
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Zhang%20Y
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsbas
DbLabel: BASE
An: edsbas.48773CD5
RelevancyScore: 944
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 943.653564453125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Yuxiao+Zhang%22">Yuxiao Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Ming+Ding%22">Ming Ding</searchLink><br /><searchLink fieldCode="AR" term="%22Hanting+Yang%22">Hanting Yang</searchLink><br /><searchLink fieldCode="AR" term="%22Yingjie+Niu%22">Yingjie Niu</searchLink><br /><searchLink fieldCode="AR" term="%22Yan+Feng%22">Yan Feng</searchLink><br /><searchLink fieldCode="AR" term="%22Kento+Ohtani%22">Kento Ohtani</searchLink><br /><searchLink fieldCode="AR" term="%22Kazuya+Takeda%22">Kazuya Takeda</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: Sensors, Vol 23, Iss 21, p 8660 (2023)
– Name: Publisher
  Label: Publisher Information
  Group: PubInfo
  Data: MDPI AG
– Name: DatePubCY
  Label: Publication Year
  Group: Date
  Data: 2023
– Name: Subset
  Label: Collection
  Group: HoldingsInfo
  Data: Directory of Open Access Journals: DOAJ Articles
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22LiDAR+point+cloud+processing%22">LiDAR point cloud processing</searchLink><br /><searchLink fieldCode="DE" term="%22snow+noise+removal%22">snow noise removal</searchLink><br /><searchLink fieldCode="DE" term="%22snow+effect+generation%22">snow effect generation</searchLink><br /><searchLink fieldCode="DE" term="%22CycleGAN%22">CycleGAN</searchLink><br /><searchLink fieldCode="DE" term="%22Chemical+technology%22">Chemical technology</searchLink><br /><searchLink fieldCode="DE" term="%22TP1-1185%22">TP1-1185</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the ‘L-DIG’ (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: article in journal/newspaper
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: NoteTitleSource
  Label: Relation
  Group: SrcInfo
  Data: https://www.mdpi.com/1424-8220/23/21/8660; https://doaj.org/toc/1424-8220; https://doaj.org/article/edee217b7c614c6193fc25be77b62309
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.3390/s23218660
– Name: URL
  Label: Availability
  Group: URL
  Data: https://doi.org/10.3390/s23218660<br />https://doaj.org/article/edee217b7c614c6193fc25be77b62309
– Name: AN
  Label: Accession Number
  Group: ID
  Data: edsbas.48773CD5
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.48773CD5
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.3390/s23218660
    Languages:
      – Text: English
    Subjects:
      – SubjectFull: LiDAR point cloud processing
        Type: general
      – SubjectFull: snow noise removal
        Type: general
      – SubjectFull: snow effect generation
        Type: general
      – SubjectFull: CycleGAN
        Type: general
      – SubjectFull: Chemical technology
        Type: general
      – SubjectFull: TP1-1185
        Type: general
    Titles:
      – TitleFull: L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yuxiao Zhang
      – PersonEntity:
          Name:
            NameFull: Ming Ding
      – PersonEntity:
          Name:
            NameFull: Hanting Yang
      – PersonEntity:
          Name:
            NameFull: Yingjie Niu
      – PersonEntity:
          Name:
            NameFull: Yan Feng
      – PersonEntity:
          Name:
            NameFull: Kento Ohtani
      – PersonEntity:
          Name:
            NameFull: Kazuya Takeda
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-locals
              Value: edsbas
            – Type: issn-locals
              Value: edsbas.oa
          Titles:
            – TitleFull: Sensors, Vol 23, Iss 21, p 8660 (2023
              Type: main
ResultId 1