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arXiv · 1802.07351

Devon: Deformable Volume Network for Learning Optical Flow

Yao Lu·Jack Valmadre·Heng Wang·Juho Kannala·Mehrtash Harandi·Philip H. S. Torr
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Abstract

State-of-the-art neural network models estimate large displacement optical flow in multi-resolution and use warping to propagate the estimation between two resolutions. Despite their impressive results, it is known that there are two problems with the approach. First, the multi-resolution estimation of optical flow fails in situations where small objects move fast. Second, warping creates artifacts when occlusion or dis-occlusion happens. In this paper, we propose a new neural network module, Deformable Cost Volume, which alleviates the two problems. Based on this module, we designed the Deformable Volume Network (Devon) which can estimate multi-scale optical flow in a single high resolution. Experiments show Devon is more suitable in handling small objects moving fast and achieves comparable results to the state-of-the-art methods in public benchmarks.

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Yao Lu, Jack Valmadre, Heng Wang, Juho Kannala, Mehrtash Harandi, Philip H. S. Torr. 2018-02-20. Devon: Deformable Volume Network for Learning Optical Flow. https://arxiv.org/abs/1802.07351

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