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A Guide to Structureless Visual Localization.

Vojtech Panek^1,2, Qunjie Zhou³, Yaqing Ding⁴

¹Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic.

International Journal of Computer Vision

|May 12, 2026

Summary

This summary is machine-generated.

Structureless visual localization methods offer flexibility for dynamic scenes, outperforming pose regression techniques. Classical geometric reasoning enhances pose accuracy, though structure-based methods remain slightly more accurate.

Keywords:

Pose Regression Pose Triangulation Visual Localization

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Area of Science:

Computer Vision
Robotics
3D Reconstruction

Background:

Visual localization estimates camera pose in known scenes, crucial for autonomous systems.
Structure-based methods use 3D models but lack flexibility for scene changes.
Structureless methods use image databases, offering easier updates but less research focus.

Purpose of the Study:

To provide the first comprehensive discussion and comparison of structureless visual localization methods.
To evaluate the performance of structureless approaches against established and recent techniques.

Main Methods:

Comparative analysis of various structureless visual localization algorithms.
Extensive experiments evaluating pose accuracy based on geometric reasoning.
Benchmarking against structure-based and pose regression methods.

Main Results:

Structureless methods utilizing classical geometric reasoning achieve higher pose accuracy.
Classical absolute or semi-generalized relative pose estimation outperforms pose regression.
Structureless methods offer flexibility at a slight cost to pose accuracy compared to structure-based methods.

Conclusions:

Structureless visual localization is a viable and flexible alternative, especially when scene dynamics are frequent.
Classical geometric approaches are key to maximizing accuracy in structureless localization.
Future work should focus on bridging the accuracy gap between structureless and structure-based methods.