Robust parameter estimation is a crucial task in several 3D computer vision pipelines such as Structure from Motion (SfM). State-of-the-art algorithms for robust estimation, however, still suffer from difficulties in converging to satisfactory solutions due to the presence of many poor local minima or flat regions in the optimization landscapes. In this paper, we introduce two novel approaches for robust parameter estimation. The first algorithm utilizes the Filter Method (FM), which is a framework for constrained optimization allowing great flexibility in algorithmic choices, to derive an adaptive kernel scaling strategy that enjoys a strong ability to escape poor minima and achieves fast convergence rates. Our second algorithm combines a generalized Majorization Minimization (GeMM) framework with the half-quadratic lifting formulation to obtain a simple yet efficient solver for robust estimation. We empirically show that both proposed approaches show encouraging capability on avoiding poor local minima and achieve competitive results compared to existing state-of-the art robust fitting algorithms.

翻译：强力参数估算是几条3D计算机视觉管道中的关键任务,如“动态结构”(SfM) 。 用于稳健估算的先进算法,然而,由于在优化地貌中存在许多贫瘠的本地微型或平地,因此在向令人满意的解决方案融合方面仍然困难重重。 在本文中,我们引入了两种稳健参数估算的新办法。 第一种算法利用了“过滤法”(FM),这是在算法选择方面允许巨大灵活性的有限优化框架,以得出适应性内核缩放战略,这种战略具有强大的能力,能够摆脱贫穷的微型模型,并实现快速趋同率。我们的第二个算法将“普遍多数化”框架与半赤道升降法组合起来,以获得简单而有效的求得稳健估算的解决方案。我们的经验显示,拟议的两种方法都显示出了鼓励避免本地小型贫困的能力,并取得与现有艺术强健的优化算法相比的竞争结果。