Citation: Furmonas, J.; Liobe, J.;
Barzdenas, V. Analytical Review of
Event-Based Camera Depth
Estimation Methods and Systems.
Sensors 2022, 22, 1201. https://
doi.org/10.3390/s22031201
Academic Editors: Yangquan Chen,
Subhas Mukhopadhyay,
Nunzio Cennamo, M. Jamal Deen,
Junseop Lee and Simone Morais
Received: 23 December 2021
Accepted: 31 January 2022
Published: 5 February 2022
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Review
Analytical Review of Event-Based Camera Depth Estimation
Methods and Systems
Justas Furmonas, John Liobe * and Vaidotas Barzdenas *
Department of Computer Science and Communications Technologies, Vilnius Gediminas Technical University,
03227 Vilnius, Lithuania; justas.furmonas@stud.vilniustech.lt
* Correspondence: john-charles.liobe@vilniustech.lt (J.L.); vaidotas.barzdenas@vilniustech.lt (V.B.)
Abstract:
Event-based cameras have increasingly become more commonplace in the commercial
space as the performance of these cameras has also continued to increase to the degree where they
can exponentially outperform their frame-based counterparts in many applications. However, instan-
tiations of event-based cameras for depth estimation are sparse. After a short introduction detailing
the salient differences and features of an event-based camera compared to that of a traditional,
frame-based one, this work summarizes the published event-based methods and systems known to
date. An analytical review of these methods and systems is performed, justifying the conclusions
drawn. This work is concluded with insights and recommendations for further development in the
field of event-based camera depth estimation.
Keywords: event-based camera; neuromorphic; depth estimation; monocular
1. Introduction
Computer vision has been one of the most popular research areas for many years.
Numerous applications exist where computer vision plays an important role, e.g., machine
inspection, photogrammetry, medical imaging, automotive safety, etc. [
1
]. These appli-
cations each incur disparate problems though common methods have been utilized to
solve these problems. For most machine vision applications neural networks have been
employed, and through the years different frameworks have been created to help solve
various problems faster and more accurately. In addition, numerous databases have been
made available online that can train any neural network to solve most machine vision
problems precisely without any additional training. Thus, computer vision has grown to a
mature level and has been applied in a broad spectrum of fields.
On the other hand, computer vision to this day has extensively utilized frame-based
cameras which have existed for many more years than computer vision itself. A frame-
based camera outputs data corresponding to the captured light intensity at every selected
pixel synchronously. This technology has been effective, and for many years has proven to
be superior to any other camera type. Nevertheless, for many applications, frame-based
cameras have features that impact performance and accuracy. Frame-based cameras suffer
from high latency, low dynamic range, and in some cases high power consumption. For
example, when using a frame-based camera to capture high-speed motion, the captured
images will exhibit motion blur which would make image processing impossible or at
the very least degrade the processing accuracy. Some solutions exist that can remove or
mitigate motion blur or substitute it with motion flow using deep neural networks [
2
].
From a hardware perspective, another way to mitigate motion blur would be to increase the
frame speed of a camera. However, this is not a trivial task. Besides the increased camera
power consumption associated with operating at a higher frame rate, the data handling
requirements of the associated image processor or digital signal processor (DSP) increase
exponentially as well. Hence, frame-based cameras for many computer vision applications
have significant challenges which have been difficult to overcome.
Sensors 2022, 22, 1201. https://doi.org/10.3390/s22031201 https://www.mdpi.com/journal/sensors
