SCFlow: Optical Flow Estimation for Spiking Camera

• URL: http://arxiv.org/abs/2110.03916v1
• Date: Fri, 8 Oct 2021 06:16:45 GMT
• Title: SCFlow: Optical Flow Estimation for Spiking Camera
• Authors: Liwen Hu, Rui Zhao, Ziluo Ding, Ruiqin Xiong, Lei Ma and Tiejun Huang
• Abstract summary: Spiking camera has enormous potential in real applications, especially for motion estimation in high-speed scenes. Optical flow estimation has achieved remarkable success in image-based and event-based vision, but % existing methods cannot be directly applied in spike stream from spiking camera. This paper presents, SCFlow, a novel deep learning pipeline for optical flow estimation for spiking camera.
• Score: 50.770803466875364
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As a bio-inspired sensor with high temporal resolution, Spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. Optical flow estimation has achieved remarkable success in image-based and event-based vision, but % existing methods cannot be directly applied in spike stream from spiking camera. conventional optical flow algorithms are not well matched to the spike stream data. This paper presents, SCFlow, a novel deep learning pipeline for optical flow estimation for spiking camera. Importantly, we introduce an proper input representation of a given spike stream, which is fed into SCFlow as the sole input. We introduce the \textit{first} spiking camera simulator (SPCS). Furthermore, based on SPCS, we first propose two optical flow datasets for spiking camera (SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively) corresponding to random high-speed and well-designed scenes. Empirically, we show that the SCFlow can predict optical flow from spike stream in different high-speed scenes, and express superiority to existing methods on the datasets. \textit{All codes and constructed datasets will be released after publication}.

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