期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2006
卷号：XXXVI Part 3
出版社：Copernicus Publications
摘要：GOODSACis a paradigm for estimation of model parameters given measurements that are contaminated by outliers. Thus, it is analternative to the well knownRANS ACstrategy.GOODSAC's search for a proper set of inliers does not only maximize the sheer size ofthis set, but also takes other assessments for the utility into account. Assessments can be used on many levels of the process to controlthe search and foster precision and proper utilization of the computational resources. This contribution discusses and compares the twomethods. In particular, the estimation of essential matrices is used as example. The comparison is performed on synthetic and real dataand is based on standard statistical methods, whereGOODSACachieves higher precision thanRANS AC
关键词：essential matrix; robust estimation; RANSAC; structure from motion
