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运动矢量来生成经过余弦一致性滤波的密集亚像素对应关系和短轨迹。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，其运行性能与顺序SIFT相当，并能以具有竞争力的成对几何精度产生更密集的匹配点。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；束调整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。