Humans can orient themselves in their 3D environments using simple 2D maps. Differently,
algorithms for visual localization mostly rely on complex 3D point clouds that are expensive …

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 …

Semantic 2D maps are commonly used by humans and machines for navigation purposes,
whether it's walking or driving. However, these maps have limitations: they lack detail, often …

Deep learning based localization and mapping has recently attracted significant attention.
Instead of creating hand-designed algorithms through exploitation of physical models or …

What will the future be? We wonder! In this survey, we explore the gap between current
research in egocentric vision and the ever-anticipated future, where wearable computing …

Learning-based visual relocalizers exhibit leading pose accuracy, but require hours or days
of training. Since training needs to happen on each new scene again, long training times …

Many 3D vision systems localize cameras within a scene using 3D point clouds. Such point
clouds are often obtained using structure from motion (SfM), after which the images are …

Deep-learning-based localization and mapping approaches have recently emerged as a
new research direction and receive significant attention from both industry and academia …

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 …

Inspired by properties of semantic segmentation, in this paper we investigate how to
leverage robust image segmentation in the context of privacy-preserving visual localization …