Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
摘要
We present a global optimization approach to optical flow estimation. The approach optimizes a classical optical flow objective over the full space of mappings between discrete grids. No descriptor matching is used. The highly regular structure of the space of mappings enables optimizations that reduce the computational complexity of the algorithm's inner loop from quadratic to linear and support efficient matching of tens of thousands of nodes to tens of thousands of displacements. We show that one-shot global optimization of a classical Horn-Schunck-type objective over regular grids at a single resolution is sufficient to initialize continuous interpolation and achieve state-of-the-art performance on challenging modern benchmarks.
更多查看译文
关键词
continuous interpolation,Horn-Schunck-type objective,computational complexity reduction,discrete grids,mappings,regular grids,global optimization,optical flow estimation,full flow
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要