mlsanityData to 7 Oct 2026

Leveraging AV1 motion vectors for Fast and Dense Feature Matching

arXiv 2510.17434 ↗First posted 20 Oct 2025cs.CVcited by 2

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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

  1. 2019SuperGlue: Learning Feature Matching with Graph Neural Networkscited by 1,437
  2. 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
  3. 2023LightGlue: Local Feature Matching at Light Speedcited by 583
  4. 2020DISK: Learning local features with policy gradientcited by 310
  5. 2020A Technical Overview of AV1cited by 46

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.