利用AV1运动矢量实现快速稠密特征匹配，节省计算资源。
Leveraging AV1 motion vectors for Fast and Dense Feature Matching
- 复用AV1编码的运动矢量生成亚像素级对应点
- 117帧视频重建出0.46-0.62百万点，重投影误差0.51-0.53像素
- 适合资源受限场景的视觉里程计与稀疏重建
本文重新利用AV1运动矢量生成稠密亚像素对应关系和由余弦一致性筛选的短轨迹。在短视频上，该压缩域前端的运行效率可媲美顺序SIFT，但所需CPU资源远少于后者，并获得更密集的匹配结果及具有竞争力的成对几何精度。以117帧片段为例，基于运动矢量匹配的SfM能成功注册所有图像，重建0.46至0.62百万个点，重投影误差为0.51至0.53像素；优化时间随匹配密度增加而上升。结果表明，压缩域对应关系是实用且资源高效的前端方案，具备在完整流程中进一步扩展的清晰路径。
原文摘要 · Abstract (English)
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.
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