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.

翻译：我们重新利用AV1运动矢量来生成密集亚像素对应关系，并通过余弦一致性过滤得到短轨迹。在短视频处理中，这种压缩域前端在CPU使用量显著降低的同时，达到了与顺序SIFT相当的性能，并以具有竞争力的成对几何精度产生更密集的匹配。在一个117帧片段的小型SfM演示中，运动矢量匹配成功配准了所有图像，并以0.51-0.53像素的重投影误差重建了46-62万个点；光束法平差时间随匹配密度增加而增长。这些结果表明，压缩域对应关系是一种实用且资源高效的前端处理方法，在完整流程中具备明确的扩展路径。