Samenvatting

This paper investigates the benefits of incorporating point visibility information of 3D point clouds within a deep learning framework, using occlusion-aware 3D priors. The presented methods for deriving the visibility of each point rely on ray-casting techniques, making the proposed solution generic and sensor independent. We demonstrate the benefits of integrating point visibility using two real-world applications. In a first application, a novel data augmentation technique is proposed leveraging occlusion-aware CAD 3D priors, resulting in state-of-the-art 3D vehicle detection. In a second application, we integrate visibility information into a vehicle pose estimation pipeline based on 3D priors. The presented techniques achieve state-of-the-art performance, significantly improving both translation and rotation accuracy on the Apollo3DCar dataset.
Originele taal-2English
Titel2023 IEEE 25th International Workshop on Multimedia Signal Processing, MMSP 2023
UitgeverijIEEE
Pagina's1-6
Aantal pagina's6
ISBN van elektronische versie979-8-3503-3893-5
DOI's
StatusPublished - 27 sep 2023
EvenementIEEE 25th International Workshop on Multimedia Signal Processing - Poitiers, France
Duur: 27 sep 202329 sep 2023

Publicatie series

Naam2023 IEEE 25th International Workshop on Multimedia Signal Processing, MMSP 2023

Conference

ConferenceIEEE 25th International Workshop on Multimedia Signal Processing
Verkorte titelIEEE MMSP 2023
Land/RegioFrance
StadPoitiers
Periode27/09/2329/09/23

Bibliografische nota

Publisher Copyright:
© 2023 IEEE.

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