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In\nparticular, we propose an input representation of the events in the form of a\ndiscretized volume that maintains the temporal distribution of the events,\nwhich we pass through a neural network to predict the motion of the events.\nThis motion is used to attempt to remove any motion blur in the event image. We\nthen propose a loss function applied to the motion compensated event image that\nmeasures the motion blur in this image. We train two networks with this\nframework, one to predict optical flow, and one to predict egomotion and\ndepths, and evaluate these networks on the Multi Vehicle Stereo Event Camera\ndataset, along with qualitative results from a variety of different scenes.","url_abs":"http://arxiv.org/abs/1812.08156v1","url_pdf":"http://arxiv.org/pdf/1812.08156v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unsupervised-event-based-learning-of-optical","repo_url":"https://github.com/TimoStoff/events_contrast_maximization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"unsupervised-event-based-learning-of-optical","repo_url":"https://github.com/mingyip/Motion_Compensated_FlowNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.08156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.08156"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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