Identificador persistente para citar o vincular este elemento:
https://accedacris.ulpgc.es/handle/10553/139745
Campo DC | Valor | idioma |
---|---|---|
dc.contributor.author | Mohamed, Sondos | en_US |
dc.contributor.author | Zimmer, Walter | en_US |
dc.contributor.author | Greer, Ross | en_US |
dc.contributor.author | Ghita, Ahmed Alaaeldin | en_US |
dc.contributor.author | Castrillón-Santana, Modesto | en_US |
dc.contributor.author | Trivedi, Mohan | en_US |
dc.contributor.author | Knoll, Alois | en_US |
dc.contributor.author | Carta, Salvatore Mario | en_US |
dc.contributor.author | Marras, Mirko | en_US |
dc.date.accessioned | 2025-06-09T12:03:26Z | - |
dc.date.available | 2025-06-09T12:03:26Z | - |
dc.date.issued | 2025 | en_US |
dc.identifier.isbn | 9783031918124 | en_US |
dc.identifier.issn | 0302-9743 | en_US |
dc.identifier.other | Scopus | - |
dc.identifier.uri | https://accedacris.ulpgc.es/handle/10553/139745 | - |
dc.description.abstract | Accurately detecting 3D objects from monocular images in dynamic roadside scenarios remains a challenging problem due to varying camera perspectives and unpredictable scene conditions. This paper introduces a two-stage training strategy to address these challenges. Our approach initially trains a model on the large-scale synthetic dataset, RoadSense3D, which offers a diverse range of scenarios for robust feature learning. Subsequently, we fine-tune the model on a combination of real-world datasets to enhance its adaptability to practical conditions. Experimental results of the Cube R-CNN model on challenging public benchmarks show a remarkable improvement in detection performance, with a mean average precision rising from 0.26 to 12.76 on the TUM Traffic A9 Highway dataset and from 2.09 to 6.60 on the DAIR-V2X-I dataset, when performing transfer learning. Code, data, and qualitative video results are available at https://roadsense3d.github.io. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.source | Lecture Notes in Computer Science[ISSN 0302-9743],v. 15630 LNCS, p. 309-325, (Enero 2025) | en_US |
dc.subject | 33 Ciencias tecnológicas | en_US |
dc.subject.other | Intelligent Transportation Systems | en_US |
dc.subject.other | Intelligent Vehicles | en_US |
dc.subject.other | Monocular 3D Object Detection | en_US |
dc.subject.other | Synthetic Data | en_US |
dc.subject.other | Transfer Learning | en_US |
dc.title | Transfer Learning from Simulated to Real Scenes for Monocular 3D Object Detection | en_US |
dc.type | info:eu-repo/semantics/conferenceObject | en_US |
dc.type | ConferenceObject | en_US |
dc.relation.conference | Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024 | en_US |
dc.identifier.doi | 10.1007/978-3-031-91813-1_20 | en_US |
dc.identifier.scopus | 105006880501 | - |
dc.contributor.orcid | 0009-0002-5647-6895 | - |
dc.contributor.orcid | 0000-0003-4565-1272 | - |
dc.contributor.orcid | 0000-0001-8595-0379 | - |
dc.contributor.orcid | 0000-0003-3702-8042 | - |
dc.contributor.orcid | 0000-0002-8673-2725 | - |
dc.contributor.orcid | 0000-0002-0937-6771 | - |
dc.contributor.orcid | 0000-0003-4840-076X | - |
dc.contributor.orcid | 0000-0001-9481-511X | - |
dc.contributor.orcid | 0000-0003-1989-6057 | - |
dc.contributor.authorscopusid | 57193795607 | - |
dc.contributor.authorscopusid | 57607056800 | - |
dc.contributor.authorscopusid | 57220950660 | - |
dc.contributor.authorscopusid | 58898119100 | - |
dc.contributor.authorscopusid | 57218418238 | - |
dc.contributor.authorscopusid | 7103153314 | - |
dc.contributor.authorscopusid | 7102250639 | - |
dc.contributor.authorscopusid | 59734046700 | - |
dc.contributor.authorscopusid | 59469344200 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.description.lastpage | 325 | en_US |
dc.description.firstpage | 309 | en_US |
dc.relation.volume | 15630 LNCS | en_US |
dc.investigacion | Ingeniería y Arquitectura | en_US |
dc.type2 | Actas de congresos | en_US |
dc.utils.revision | Sí | en_US |
dc.date.coverdate | Enero 2025 | en_US |
dc.identifier.conferenceid | events155916 | - |
dc.identifier.ulpgc | Sí | en_US |
dc.contributor.buulpgc | BU-INF | en_US |
dc.description.sjr | 0,606 | |
dc.description.sjrq | Q2 | |
dc.description.miaricds | 10,0 | |
item.grantfulltext | none | - |
item.fulltext | Sin texto completo | - |
crisitem.author.dept | GIR SIANI: Inteligencia Artificial, Robótica y Oceanografía Computacional | - |
crisitem.author.dept | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.dept | Departamento de Informática y Sistemas | - |
crisitem.author.orcid | 0000-0002-8673-2725 | - |
crisitem.author.parentorg | IU Sistemas Inteligentes y Aplicaciones Numéricas | - |
crisitem.author.fullName | Castrillón Santana, Modesto Fernando | - |
crisitem.event.eventsstartdate | 29-09-2024 | - |
crisitem.event.eventsenddate | 04-10-2024 | - |
Colección: | Actas de congresos |
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