DCVNet for improved optical flow pixel tracking in video
DCVNet for improved optical flow pixel tracking in video
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow
arXiv paper abstract https://arxiv.org/abs/2103.17271
arXiv PDF paper https://arxiv.org/pdf/2103.17271.pdf
Optical flow, as a dense matching problem, is about estimating every single pixel’s displacement between two consecutive video frames, capturing the motion of brightness patterns.
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By combining the dilated cost volumes and 3D convolutions, our proposed model DCVNet not only exhibits real-time inference (71 fps on a mid-end 1080ti GPU) but is also compact and obtains comparable accuracy to existing approaches.
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