论文标题
可伸缩的集群一致性统计信息可靠的多对象匹配
Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching
论文作者
论文摘要
我们开发了新的统计信息，以通过运动管道从结构中进行稳健过滤损坏的关键点匹配。统计数据基于在关键点匹配图的群集结构中产生的一致性约束。统计数据旨在为损坏的比赛和未腐败的比赛提供较小的值。这些新的统计数据与迭代重新加权方案结合使用，以滤波关键点，然后可以通过运动管道将其馈入任何标准结构。该过滤方法可以有效地实现并缩放到大量数据集，因为它仅需要稀疏的矩阵乘法。我们证明了该方法对运动数据集的合成和真实结构的功效，并表明它在这些任务中实现了最先进的准确性和速度。
We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline. The statistics are based on consistency constraints that arise within the clustered structure of the graph of keypoint matches. The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches. These new statistics are combined with an iterative reweighting scheme to filter keypoints, which can then be fed into any standard structure from motion pipeline. This filtering method can be efficiently implemented and scaled to massive datasets as it only requires sparse matrix multiplication. We demonstrate the efficacy of this method on synthetic and real structure from motion datasets and show that it achieves state-of-the-art accuracy and speed in these tasks.
