Localization and mapping is the foundational technology for augmented reality (AR) that
enables sharing and persistence of digital content in the real world. While significant …

Modern computer vision services often require users to share raw feature descriptors with an
untrusted server. This presents an inherent privacy risk, as raw descriptors may be used to …

In this paper, we propose to go beyond the well-established approach to vision-based
localization that relies on visual descriptor matching between a query image and a 3D point …

In the light of recent analyses on privacy-concerning scene revelation from visual
descriptors, we develop descriptors that conceal the input image content. In particular, we …

Recent methods on privacy-preserving image-based localization use a random line
parameterization to protect the privacy of query images and database maps. The lifting of …

Recently proposed privacy preserving solutions for cloud-based localization rely on lifting
traditional point-based maps to randomized 3D line clouds. While the lifted representation is …

We propose an approach for estimating the relative pose between calibrated image pairs by
jointly exploiting points, lines, and their coincidences in a hybrid manner. We investigate all …

Visual localization refers to the process of recovering camera pose from input image relative
to a known scene, forming a cornerstone of numerous vision and robotics systems. While …

Deep networks are susceptible to adversarial attacks. End-to-end differentiability of deep
networks provides the analytical formulation which has aided in proliferation of diverse …

Rapid growth in the popularity of AR/VR/MR applications and cloud-based visual
localization systems has given rise to an increased focus on the privacy of user content in …