Cluster-Wise Ratio Tests for Fast Camera Localization

TL;DR

This paper introduces a fast, scalable camera localization method that uses cluster-wise ratio tests and global approximate nearest neighbor search to improve matching efficiency and accuracy in large-scale environments.

Contribution

It proposes a novel coarse-to-fine strategy with approximate ratio tests and cluster-based back-matching, enhancing scalability and robustness in camera localization.

Findings

Achieves state-of-the-art localization accuracy on benchmark datasets.

Outperforms more complex methods with simpler, efficient algorithms.

Effectively handles large-scale, repetitive-structure environments.

Abstract

Feature point matching for camera localization suffers from scalability problems. Even when feature descriptors associated with 3D scene points are locally unique, as coverage grows, similar or repeated features become increasingly common. As a result, the standard distance ratio-test used to identify reliable image feature points is overly restrictive and rejects many good candidate matches. We propose a simple coarse-to-fine strategy that uses conservative approximations to robust local ratio-tests that can be computed efficiently using global approximate k-nearest neighbor search. We treat these forward matches as votes in camera pose space and use them to prioritize back-matching within candidate camera pose clusters, exploiting feature co-visibility captured by clustering the 3D model camera pose graph. This approach achieves state-of-the-art camera localization results on a variety of popular benchmarks, outperforming several methods that use more complicated data structures and that make more restrictive assumptions on camera pose. We also carry out diagnostic analyses on a difficult test dataset containing globally repetitive structure that suggest our approach successfully adapts to the challenges of large-scale image localization.