Semantic-Aware Label Placement for Augmented Reality in Street View

TL;DR

This paper presents a semantic-aware label placement method for augmented reality street view applications, improving label readability and reducing occlusion of important real-world objects by integrating saliency, semantic, and task-specific information.

Contribution

It introduces a novel guidance map and an optimization-based placement approach, leveraging user-labeled data to enhance label positioning in AR street view scenarios.

Findings

Outperforms previous label placement methods in AR street view tasks

Reduces occlusion of critical real-world objects by labels

Enhances label readability and user experience

Abstract

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.