Mostrar o rexistro simple do ítem
Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks
dc.contributor.author | Silva, Manuel | |
dc.contributor.author | Mures, Omar A. | |
dc.contributor.author | Seoane, Antonio | |
dc.contributor.author | Iglesias-Guitian, Jose A. | |
dc.date.accessioned | 2023-11-08T20:04:58Z | |
dc.date.available | 2023-11-08T20:04:58Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/2183/34119 | |
dc.description | Cursos e Congresos , C-155 | es_ES |
dc.description.abstract | [Abstract] Deep neural networks are well known for demanding large amounts of training data, motivating the appearance of multiple synthetic datasets covering multiple domains. However, synthetic datasets have not yet outperformed real data for autonomous driving applications, particularly for semantic segmentation tasks. Thus, a deeper comprehension about how the parameters involved in synthetic data generation could help in creating better synthetic datasets. This work provides a summary review of prior research covering how image noise, camera noise and rendering photorealism could affect learning tasks. Furthermore, we presents novel experiments aimed at advancing our understanding around generating synthetic data for autonomous driving neural networks aimed at semantic segmentation | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431F 2021/11 | es_ES |
dc.description.sponsorship | This work has been supported by the Spanish Ministry of Science and Innovation (AEI/PID2020-115734RB-C22). We also want to acknowledge Side Effects Software Inc. for their support to this work. J.A. Iglesias-Guitian also acknowledges the UDC-Inditex InTalent programme, the Ministry of Science and Innovation (AEI/RYC2018-025385-I) and Xunta de Galicia (ED431F 2021/11). CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS) | |
dc.language.iso | eng | es_ES |
dc.publisher | Universidade da Coruña, Servizo de Publicacións | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115734RB-C22/ES/GENERACIÓN PROCEDURAL DE ESCENARIOS AUMENTADOS CON ANOTACIÓN DE DATOS AUTOMÁTICA | es_ES |
dc.relation.uri | https://doi.org/10.17979/spudc.000024.47 | |
dc.rights | Attribution 4.0 International (CC BY 4.0) | es_ES |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/deed.es | * |
dc.subject | Redes neuronales (Informática) | es_ES |
dc.subject | Datos sintéticos | es_ES |
dc.subject | Segmentación semántica | es_ES |
dc.title | Understanding the Influence of Rendering Parameters in Synthetic Datasets for Neural Semantic Segmentation Tasks | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.startPage | 313 | es_ES |
UDC.endPage | 319 | es_ES |
UDC.conferenceTitle | VI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruña | es_ES |