Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids
This addresses the problem of efficient and accurate optical flow estimation for computer vision applications, representing an incremental improvement with a novel optimization method.
The paper tackles optical flow estimation by introducing a global optimization approach over regular grids, achieving state-of-the-art performance on modern benchmarks without descriptor matching.
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.