Semantic-Aware Label Placement for Augmented Reality in Street View
This work addresses label placement challenges for users in AR street view navigation, though it is incremental as it builds on existing optimization and semantic techniques.
The paper tackles the problem of placing labels clearly without occluding critical information in augmented reality street view applications by introducing a semantic-aware method that integrates saliency, semantic information, and task-specific priors into a guidance map, resulting in improved label layouts that reduce overlay on important objects.
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.