Guided sparse feature matching via coarsely defined dense matches

A system for game status detection, comprising:

• a dense feature estimator to execute 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 and calculate a neighborhood correspondence based on the first set of feature correspondences for each pixel in a first image of the image pair;
• a sparse feature estimator to execute 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;
• and
• a refinement manager to refine 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.

At least one non-transitory computer-readable medium, comprising instructions to direct a processor to:

• execute 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;
• calculate a neighborhood correspondence based on the first set of feature correspondences for each pixel in a first image of the image pair;
• execute 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;
• and
• refine 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.