A Fusion Approach for Multi-Frame Optical Flow Estimation

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Authors Deqing Sun, Zhile Ren, Erik B. Sudderth, Ming-Hsuan Yang, Jan Kautz, Orazio Gallo
Journal/Conference Name Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019
Paper Category
Paper Abstract To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks. Our models will be available on https//github.com/NVlabs/PWC-Net.
Date of publication 2018
Code Programming Language Python
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