Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow Linchao Bao Qingxiong Yang City University of Hong Kong
Introduction We use Nearest Neighbor Field (NNF) for optical flow estimation No distance limitation, can handle large displacements Problem 1: Directly applying existing NNF algorithm for optical flow estimation will result in erroneous motion boundaries (see figure below) Problem 2: Need large patch size to eliminate matching ambiguities for spatially coherent flow fields (speed concern) Problem 3: Need subpixel accuracy
Edge-Preserving PatchMatch Traditional NNF (Euclidean Patch Distance)
Motion boundary well preserved
ws Spatial weight wr Range weight
Bilateral Weighting
Fast NNF algorithm: PatchMatch (others not applicable)
Initialization
Propagation
Fast Approximate Algorithm Use a few pixels (50 to 100) to represent large patch (35x35) Select representative pixels according similarity to center pixel A novel randomized algorithm for this selection process (see figure below) Inspired by PatchMatch Random initialization, then iteratively propagate and merge Could be applied to other applications like bilateral filtering approximation
Contributions A fast optical flow algorithm that can handle large motion A novel self-similarity propagation algorithm A high-accuracy subpixel refinement algorithm
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Results
Ground Truth Erroneous motion boundary
Our NNF (Edge-Preserving Patch Distance)
Hailin Jin Adobe Research
IEEE 2014 Conference on Computer Vision and Pattern Recognition
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Still too slow for large patch size with bilateral weighting
The column “EPE s40+” means error over large-motion regions Image size is 1024x436 (0.45 Megapixel) The runtime are reproduced from reported on Middlebury. Note that not all of them are reported on GPU. Our result is reported on a GTX 780 GPU
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