Reducing the Sim-to-Real Gap for Event Cameras
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Stoffregen, Timo; Scheerlinck, Cedric; Scaramuzza, Davide; Drummond, Tom; Barnes, Nick; Kleeman, Lindsay; Mahony, Robert
Description
Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called ‘events’ with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would fail. Recent work has demonstrated impressive results using Convolutional Neural Networks (CNNs) for video reconstruction and optic flow with events. We present strategies for improving training data for event based CNNs that result in 20–40%...[Show more]
Collections | ANU Research Publications |
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Date published: | 2020 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/317220 |
Source: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
DOI: | 10.1007/978-3-030-58583-9_32 |
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978-3-030-58583-9_32.pdf | 1.81 MB | Adobe PDF | Request a copy |
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