A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities

Valverde, Miguel; Moutinho, Alexandra; Zacchi, João-Vitor

doi:10.3390/s25175264

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A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities

by

Miguel Valverde

^1,*

,

Alexandra Moutinho

^2,*

and

João-Vitor Zacchi

³

¹

Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal

²

Instituto de Engenharia Mecânica, Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal

³

Fraunhofer IKS, 80686 Munich, Germany

^*

Authors to whom correspondence should be addressed.

Sensors 2025, 25(17), 5264; https://doi.org/10.3390/s25175264

Submission received: 7 July 2025 / Revised: 29 July 2025 / Accepted: 19 August 2025 / Published: 24 August 2025

(This article belongs to the Special Issue Sensors and Sensor Fusion Technology in Autonomous Vehicles)

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Abstract

This paper presents a comprehensive survey of deep learning-based methods for 3D object detection in autonomous driving, focusing on their use of diverse sensor modalities, including monocular cameras, stereo vision, LiDAR, radar, and multi-modal fusion. To systematically organize the literature, a structured taxonomy is proposed that categorizes methods by input modality. The review also outlines the chronological evolution of these approaches, highlighting major architectural developments and paradigm shifts. Furthermore, the surveyed methods are quantitatively compared using standard evaluation metrics across benchmark datasets in autonomous driving scenarios. Overall, this work provides a detailed and modality-agnostic overview of the current landscape of deep learning approaches for 3D object detection in autonomous driving. Results of this work are available in a github open repository.

Keywords: 3D object detection; deep learning; monocular camera; stereo vision; LiDAR; radar; sensor fusion; KITTI; nuScenes; Waymo; autonomous vehicles

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MDPI and ACS Style

Valverde, M.; Moutinho, A.; Zacchi, J.-V. A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities. Sensors 2025, 25, 5264. https://doi.org/10.3390/s25175264

AMA Style

Valverde M, Moutinho A, Zacchi J-V. A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities. Sensors. 2025; 25(17):5264. https://doi.org/10.3390/s25175264

Chicago/Turabian Style

Valverde, Miguel, Alexandra Moutinho, and João-Vitor Zacchi. 2025. "A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities" Sensors 25, no. 17: 5264. https://doi.org/10.3390/s25175264

APA Style

Valverde, M., Moutinho, A., & Zacchi, J.-V. (2025). A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities. Sensors, 25(17), 5264. https://doi.org/10.3390/s25175264

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Survey of Deep Learning-Based 3D Object Detection Methods for Autonomous Driving Across Different Sensor Modalities

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