  In an augmented reality (AR) application, placing labels in a manner that is
clear and readable without occluding the critical information from the
real-world can be a challenging problem. This paper introduces a label
placement technique for AR used in street view scenarios. We propose a
semantic-aware task-specific label placement method by identifying potentially
important image regions through a novel feature map, which we refer to as
guidance map. Given an input image, its saliency information, semantic
information and the task-specific importance prior are integrated into the
guidance map for our labeling task. To learn the task prior, we created a label
placement dataset with the users' labeling preferences, as well as use it for
evaluation. Our solution encodes the constraints for placing labels in an
optimization problem to obtain the final label layout, and the labels will be
placed in appropriate positions to reduce the chances of overlaying important
real-world objects in street view AR scenarios. The experimental validation
shows clearly the benefits of our method over previous solutions in the AR
street view navigation and similar applications.
