Leveraging AV1 motion vectors for Fast and Dense Feature Matching
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 20 Oct 2025.
Builds on
5 of 17 references.
References · 5
- 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2023LightGlue: Local Feature Matching at Light Speedcited by 583
- 2020DISK: Learning local features with policy gradientcited by 310
- 2020A Technical Overview of AV1cited by 46
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Citing papers per year: 2026: 2.
2 papers.