We present VSAC, a RANSAC-type robust estimator with a number of novelties. It benefits from the introduction of the concept of independent inliers that improves significantly the efficacy of the dominant plane handling and, also, allows near error-free rejection of incorrect models, without false positives. The local optimization process and its application is improved so that it is run on average only once. Further technical improvements include adaptive sequential hypothesis verification and efficient model estimation via Gaussian elimination. Experiments on four standard datasets show that VSAC is significantly faster than all its predecessors and runs on average in 1-2 ms, on a CPU. It is two orders of magnitude faster and yet as precise as MAGSAC++, the currently most accurate estimator of two-view geometry. In the repeated runs on EVD, HPatches, PhotoTourism, and Kusvod2 datasets, it never failed.

翻译：我们介绍了VSAC, 这是一种具有若干新颖之处的RANSAC型强力估计器,它得益于引入独立直线器概念,该概念大大提高了主要平面处理的功效,还允许近乎无误拒绝不正确的模型,而没有虚假的正数。当地优化过程及其应用得到改进,使其平均运行一次。进一步的技术改进包括适应性的先后假设核查以及通过Gaussian消除对模型的有效估计。对四个标准数据集的实验表明,VSAC比其所有前身都快得多,平均运行1-2米,在CPU上运行。它比MAGSAC++(目前最精确的双视几何测算仪)更快、更快速、更精确的两个数量级。在反复运行的EVD、HPatches、PhotoTourisism和Kusvod2数据集中,它从未失败。