  Local feature matching is a critical component of many computer vision
pipelines, including among others Structure-from-Motion, SLAM, and Visual
Localization. However, due to limitations in the descriptors, raw matches are
often contaminated by a majority of outliers. As a result, outlier detection is
a fundamental problem in computer vision, and a wide range of approaches have
been proposed over the last decades. In this paper we revisit handcrafted
approaches to outlier filtering. Based on best practices, we propose a
hierarchical pipeline for effective outlier detection as well as integrate
novel ideas which in sum lead to AdaLAM, an efficient and competitive approach
to outlier rejection. AdaLAM is designed to effectively exploit modern parallel
hardware, resulting in a very fast, yet very accurate, outlier filter. We
validate AdaLAM on multiple large and diverse datasets, and we submit to the
Image Matching Challenge (CVPR2020), obtaining competitive results with simple
baseline descriptors. We show that AdaLAM is more than competitive to current
state of the art, both in terms of efficiency and effectiveness.
