Privacy Preserving Image Queries for Camera Localization

Pablo Speciale, Johannes Schoenberger, Sudipta N. Sinha, Marc Pollefeys

Abstract:

Augmented/mixed reality and robotic applications are
increasingly relying on cloud-based localization services,
which require users to upload query images to perform camera pose estimation on a server. This raises significant privacy concerns when consumers use such services in their
homes or in confidential industrial settings. Even if only
image features are uploaded, the privacy concerns remain
as the images can be reconstructed fairly well from feature
locations and descriptors. We propose to conceal the content of the query images from an adversary on the server
or a man-in-the-middle intruder. The key insight is to replace the 2D image feature points in the query image with
randomly oriented 2D lines passing through their original
2D positions. It will be shown that this feature representation hides the image contents, and thereby protects user
privacy, yet still provides sufficient geometric constraints to
enable robust and accurate 6-DOF camera pose estimation
from feature correspondences. Our proposed method can
handle single- and multi-image queries as well as exploit
additional information about known structure, gravity, and
scale. Numerous experiments demonstrate the high practical relevance of our approach.