Intel Corporation

Niloufar Pourian

Oscar Nestares

Radka Tezaur

Techniques related to multi-level optical flow estimation are discussed. Such techniques include partitioning each pair of input images into one or more partitions, separately performing optical flow estimation on the partitions, and merging the separately generated optical flow results into a final optical flow map for the pair of input images.

Multi-level optical flow estimation framework for stereo pairs of images based on spatial partitioning

Techniques related to optical flow estimation for equirectangular images are discussed. Such techniques include combining an optical flow map generated using an input pair of equirectangular images and an optical flow map generated using a transformed pair of equirectangular images rotated with respect to the input pair to move polar regions in the input pair to central regions in the transformed pair.

Accurate optical flow estimation in stereo pairs of equirectangular images

A method and system of image processing of omnidirectional images with a viewpoint shift.

Method and system of image processing of omnidirectional images with a viewpoint shift

An example method is described herein. The method includes executing dense feature matching on an image pair that is down sampled to obtain a first set of feature correspondences for each pixel of the down sampled image pair. The method also includes calculating a neighborhood correspondence based on the first set of feature correspondences for each pixel in a first image of the image pair. Further, the method includes executing sparse feature matching on stereoscopic patch pairs from the image pair based on the neighborhood correspondence for each pixel to obtain correspondence estimates for each stereoscopic patch pair. Finally, the method includes refining the correspondence estimates for each stereoscopic patch pair to obtain a semi-dense set of feature correspondences by applying a geometric constraint to the correspondence estimates and retaining correspondences that satisfy the geometric constraint.

Guided sparse feature matching via coarsely defined dense matches

Techniques related to feature detection and matching fisheye images are discussed. Such techniques include determining a geometric constraint for the feature matching using match results from a first image based feature matching and generating resultant matches based on applying the geometric constraint and a second image based feature matching that applies a looser image based feature matching requirement than the image based feature matching.

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Niloufar Pourian