  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.
