Privacy-Preserving Structureless Visual Localization via Image Obfuscation

TL;DR

This paper introduces a simple image obfuscation method for privacy-preserving visual localization that maintains high accuracy and compatibility with existing structureless pipelines.

Contribution

It demonstrates that common image operations like segmentation can obfuscate images without needing complex modifications, preserving privacy and accuracy.

Findings

Obfuscated images can be matched effectively by modern feature matchers.

The proposed method achieves state-of-the-art accuracy among privacy-preserving localization approaches.

Simple image obfuscation techniques are compatible with existing structureless localization pipelines.

Abstract

Visual localization is the task of estimating the camera pose of an image relative to a scene representation. In practice, visual localization systems are often cloud-based. Naturally, this raises privacy concerns in terms of revealing private details through the images sent to the server or through the representations stored on the server. Privacy-preserving localization aims to avoid such leakage of private details. However, the resulting localization approaches are significantly more complex, slower, and less accurate than their non-privacy-preserving counterparts. In this paper, we consider structureless localization methods in the context of privacy preservation. Structureless methods represent the scene through a set of reference images with known camera poses and intrinsics. In contrast to existing methods proposing representations that are as privacy-preserving as possible, we study a simple image obfuscation approach based on common image operations, e.g., replacing RGB images with (semantic) segmentations. We show that existing structureless pipelines do not need any special adjustments, as modern feature matchers can match obfuscated images out of the box. The results are easy-to-implement pipelines that can ensure both the privacy of the query images and the scene representations. Detailed experiments on multiple datasets show that the resulting methods achieve state-of-the-art pose accuracy for privacy-preserving approaches.