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, instantiations 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.
Highlights
The most popular computational pieces of hardware selected for event-based camera depth-sensing implementations are central processing unit (CPU) followed by field-programmable gate arrays (FPGAs) and neuromorphic processors
The event-based camera has most recently gained traction in the commercial image sensor market as its advantages over frame-based cameras have been successfully demonstrated in some key applications
An event-based camera exhibits a significantly higher dynamic range, lower latency, and lower power consumption as compared to its frame-based brethren. These features have yet to be fully exploited in stereo or monocular depth vision applications
Summary
Analytical Review of 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. 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. Numerous databases have been made available online that can train any neural network to solve most machine vision problems precisely without any additional training. Computer vision has grown to a mature level and has been applied in a broad spectrum of fields
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