A Survey on Event Camera Simulators and Datasets for Optical Flow Estimation
O. Hageman (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Nergis Tomen – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Hesam Araghi – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
G. Lan – Graduation committee member (TU Delft - Embedded Systems)
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Abstract
Computer vision tasks have shown to benefit greatly from both developments in deep learning networks, and the emergence of event cameras. Deep networks can require a large amount of training data, which is not readily available for event cameras, specifically for optical flow estimation. The need for simulating this data in a realistic, physics-driven manner is therefore crucial. This paper compares the state of the art event camera simulators on different criteria, including event timestamp modeling, performance under low illumination, bandwidth simulation, computation speed and various types of noise simulation. We also summarize the shortcomings of some commonly used optical flow event datasets. For generating high-quality, realistic events, The V2E and DVS-Voltmeter simulators have shown to produce the most accurate data.