RT Conference Proceedings
T1 3D Object Detection for Autonomous Driving: A Practical Survey
A1 Ramajo, Álvaro
A1 Escalera Hueso, Arturo de la
A1 Armingol Moreno, José María
AB Autonomous driving has been one of the most promising research lines in the last decade. Although still far off the sought-after level 5, the research community shows great advancements in one of the most challenging tasks: the 3d perception. The rapid progress of related fields like Deep Learning is one the reasons behind this success. This enables and improves the processing algorithms for the input data provided by LiDAR, cameras, radars and such other devices used for environment perception. With such growing knowledge, reviewing and structuring the state-of-the-art solutions becomes a necessity in order to correctly address future research directions. This paper provides a comprehensive survey of the progress of 3D object detection in terms of sensor data, available datasets, top-performing architectures and most notable frameworks that serve as a baseline for current and upcoming works.
PB SCITEPRESS
SN 978-989-758-652-1
YR 2023
FD 2023-04-26
LK https://hdl.handle.net/10016/43983
UL https://hdl.handle.net/10016/43983
LA eng
NO Proceeding of: 9th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2023), Prague (Czech Republic ), 26th to 28th May 2023.
NO Grant PID2019-104793RB-C31 and PDC2021121517-C31 funded by MCIN/AEI/10.13039/50110 0011033 and by the European Union “NextGenerationEU/PRTR” and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362).
DS e-Archivo
RD 30 jun. 2024