USAC: A Universal Framework for Random Sample Consensus
USAC: A Universal Framework for Random Sample Consensus
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DOI：
10.1109/tpami.2012.257
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发表时间：
2013-08-01
影响因子：
23.6
通讯作者：
Frahm, Jan-Michael
中科院分区：
文献类型：
--
作者：
Raguram, Rahul;Chum, Ondrej;Frahm, Jan-Michael
计算机视觉中经常出现的一个计算问题是从被噪声和离群值污染的数据中估计模型的参数。更一般地说，任何试图从噪声数据测量中估计数量的实际系统，其核心都必须有一些处理数据污染的方法。随机抽样一致性（RANSAC）算法是最流行的鲁棒估计工具之一。近年来，在这一领域的活动激增，导致了许多技术的发展，提高了基本RANSAC算法的效率和鲁棒性。在本文中，我们提出了一个全面的综述，最近的研究在RANSAC为基础的抗差估计，分析和比较各种方法，已经探索了多年。我们提供了一个共同的背景下，这种分析引入了一个新的框架，我们称之为通用RANSAC（USAC）的鲁棒估计。USAC扩展了标准RANSAC的简单假设和验证结构，以纳入许多重要的实际和计算考虑因素。此外，我们提供了一个通用的C++软件库，通过利用各种模块的最先进的算法来实现USAC框架。因此，该实现解决了单个统一包内的标准RANSAC的许多限制。我们基准的算法的性能上的大量收集的估计问题。我们提供的实现可以被研究人员使用，无论是作为一个独立的工具，强大的估计或作为一个基准，用于评估新技术。
A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing and comparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques.