Leveraging AV1 motion vectors for Fast and Dense Feature Matching

Julien Zouein, Hossein Javidnia, François Pitié, Anil C. Kokaram

Abstract: We repurpose AV1 motion vectors to produce dense sub-pixel correspondences and short tracks filtered by cosine consistency. On short videos, this compressed-domain front end runs comparably to sequential SIFT while using far less CPU, and yields denser matches with competitive pairwise geometry. As a small SfM demo on a 117-frame clip, MV matches register all images and reconstruct 0.46-0.62M points at 0.51-0.53,px reprojection error; BA time grows with match density. These results show compressed-domain correspondences are a practical, resource-efficient front end with clear paths to scaling in full pipelines.

External IDs:dblp:journals/corr/abs-2510-17434