Publication:
Mono-DCNet: Monocular 3D Object Detection via Depth-based Centroid Refinement and Pose Estimation

Simple item page
carlosiii.embargo.liftdate2024-07-19
carlosiii.embargo.terms2024-07-19
dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorAstudillo Olalla, Armando
dc.contributor.authorAl Kaff, Abdulla Hussein Abdulrahman
dc.contributor.authorGarcía Fernández, Fernando
dc.date.accessioned2023-03-02T11:11:02Z
dc.date.issued2022-07-19
dc.descriptionProceeding of 2022 IEEE Intelligent Vehicles Symposium (IV), (33rd IEEE IV), 04-09 June 2022, Aachen, Germany.en
dc.description.abstract3D object detection is a well-known problem for autonomous systems. Most of the existing methods use sensor fusion techniques with Radar, LiDAR, and Cameras. However, one of the challenges is to estimate the 3D shape and location of the adjoining vehicles from a single monocular image without other 3D sensors; such as Radar or LiDAR. To solve the lack of the depth information, a novel method for 3D vehicle detection is presented. In this work, instead of using the whole depth map and the viewing angle (allocentric angle), only the depth mask of each object is used to refine the projected centroid and estimate its egocentric angle directly. The performance of the proposed method is tested and validated using the KITTI dataset, obtaining similar results to other state-of-the-art methods for Monocular 3D Object Detection.en
dc.description.sponsorshipThis work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (“Fostering Young Doctors Research”, APBI-CMUC3M), and in the context of the V PRICIT (Research and Technological Innovation Regional Programme). Also, We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.en
dc.description.statusPublicadoes
dc.format.extent5
dc.identifier.bibliographicCitation2022 IEEE Intelligent Vehicles Symposium (IV). IEEE. Pp. 664-669.en
dc.identifier.doihttps://doi.org/10.1109/IV51971.2022.9827373
dc.identifier.isbn978-1-6654-8821-1
dc.identifier.publicationfirstpage664
dc.identifier.publicationlastpage669
dc.identifier.publicationtitle2022 IEEE Intelligent Vehicles Symposium (IV)en
dc.identifier.urihttps://hdl.handle.net/10016/36723
dc.identifier.uxxiCC/0000034081
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate04-09 June 2022en
dc.relation.eventnumber33
dc.relation.eventplaceAachen, Alemania
dc.relation.eventtitleIEEE Intelligent Vehicles Symposium (IV)en
dc.rights©2022 IEEE.en
dc.rights.accessRightsembargoed accessen
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherImage segmentationen
dc.subject.otherThree-dimensional displaysen
dc.subject.otherLaser radaren
dc.subject.otherPower demanden
dc.subject.otherShapeen
dc.subject.otherIntelligent vehiclesen
dc.subject.otherPose estimationen
dc.titleMono-DCNet: Monocular 3D Object Detection via Depth-based Centroid Refinement and Pose Estimationen
dc.typeconference proceedings*
dc.type.hasVersionAM*
dspace.entity.typePublication
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
DCNet_IV_2022_ps.pdf
Size:
398.38 KB
Format:
Adobe Portable Document Format
Download
Collections
DISA - LSI - Artículos en Congresos Internacionales