Publication: 3D Object Detection for Autonomous Driving: A Practical Survey
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2023-04-26
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SCITEPRESS
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Abstract
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.
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Proceeding of: 9th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2023), Prague (Czech Republic ), 26th to 28th May 2023.
Keywords
3d Object Detection, Autonomous Driving, Deep Learning
Bibliographic citation
Ramajo Ballester, A., Escalera Hueso, A. de la, Armingol Moreno, J. M. (2023). 3D Object Detection for Autonomous Driving: A Practical Survey. Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2023). Portugal: Science and Technology Publications, pp. 64-73