Escaping Poor Local Minima in Large Scale Robust Estimation
This addresses a critical bottleneck in Structure from Motion pipelines for computer vision researchers, though it appears incremental as it builds on existing frameworks.
The paper tackles the problem of robust parameter estimation in 3D computer vision, where existing algorithms struggle with poor local minima, and introduces two novel approaches that show competitive results in escaping these minima.
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.