Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.17434 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913127925284864 |
|---|---|
| author | Zouein, Julien Javidnia, Hossein Pitié, François Kokaram, Anil |
| author_facet | Zouein, Julien Javidnia, Hossein Pitié, François Kokaram, Anil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging AV1 motion vectors for Fast and Dense Feature Matching Zouein, Julien Javidnia, Hossein Pitié, François Kokaram, Anil Computer Vision and Pattern Recognition 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. |
| title | Leveraging AV1 motion vectors for Fast and Dense Feature Matching |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2510.17434 |