Handcrafted Outlier Detection Revisited
Paper in proceedings, 2020

Local feature matching is a critical part 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, from simple checks based on descriptor similarity to geometric verification, have been proposed over the last decades. In recent years, deep learning-based approaches to outlier detection have become popular. Unfortunately, the corresponding works rarely compare with strong classical baselines. 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 an efficient and competitive approach to outlier rejection. We show that our approach, although not relying on learning, is more than competitive to both recent learned works as well as handcrafted approaches, both in terms of efficiency and effectiveness. The code is available at https://github.com/cavalli1234/AdaLAM.

Spatial verification

Low-level vision


Spatial consistency

Spatial matching


Luca Cavalli

Swiss Federal Institute of Technology in Zürich (ETH)

Viktor Larsson

Swiss Federal Institute of Technology in Zürich (ETH)

Martin Ralf Oswald

Swiss Federal Institute of Technology in Zürich (ETH)

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Marc Pollefeys

Microsoft Mixed Reality & AI Lab - Zürich

Swiss Federal Institute of Technology in Zürich (ETH)

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12364 LNCS 770-787

16th European Conference on Computer Vision, ECCV 2020
Glasgow, United Kingdom,

Subject Categories


Computer Science

Computer Vision and Robotics (Autonomous Systems)



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