A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques

Benedict Marsh, Abdul Sadka, Hamid Bahai

Research output: Contribution to journalArticlepeer-review


In this review, we provide a detailed coverage of multi-sensor fusion techniques that use RGB stereo images and a sparse LiDAR-projected depth map as input data to output a dense depth map prediction. We cover state-of-the-art fusion techniques which, in recent years, have been deep learning-based methods that are end-to-end trainable. We then conduct a comparative evaluation of the state-of-the-art techniques and provide a detailed analysis of their strengths and limitations as well as the applications they are best suited for.
Original languageEnglish
Article number9364
Issue number23
Publication statusPublished - 1 Dec 2022

Bibliographical note

© 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://


Dive into the research topics of 'A Critical Review of Deep Learning-Based Multi-Sensor Fusion Techniques'. Together they form a unique fingerprint.

Cite this