1. Repositório Aberto da Universidade do Porto
  2. FEUP - Faculdade de Engenharia
  3. FEUP - Dissertação
Utilize este identificador para referenciar este registo: https://hdl.handle.net/10216/135271
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Campo DCValorIdioma
dc.creatorBernardo Magina Madureira Palha de Araújo
dc.date.accessioned2025-11-06T08:15:26Z-
dc.date.available2025-11-06T08:15:26Z-
dc.date.issued2021-07-22
dc.date.submitted2021-07-30
dc.identifier.othersigarra:486005
dc.identifier.urihttps://hdl.handle.net/10216/135271-
dc.descriptionDeep learning on 3D LiDAR point clouds is in its infancy stages, with room to grow and improve, especially in the context of automated driving systems. A considerable amount of research has been pointed at this particular application very lately as a means to boost the performance and reliability of self-driving cars. However, the quantity of data needed to supervise perception point cloud-based models is extremely large and costly to annotate. This thesis studies, evaluates and compares state-of-the-art detection networks and label efficient learning techniques, shedding some light on how to train perception models on point clouds with less annotated data.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectEngenharia electrotécnica, electrónica e informática
dc.subjectElectrical engineering, Electronic engineering, Information engineering
dc.titleLabel-efficient learning of LiDAR-based perception models for autonomous driving
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.34626/mt86-6n26
dc.identifier.tid202818101
dc.subject.fosCiências da engenharia e tecnologias::Engenharia electrotécnica, electrónica e informática
dc.subject.fosEngineering and technology::Electrical engineering, Electronic engineering, Information engineering
thesis.degree.disciplineMestrado Integrado em Engenharia Electrotécnica e de Computadores
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
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