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 in 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.

中文翻译：

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街景中增强现实的语义感知标签放置

在增强现实 (AR) 应用程序中，在不遮挡现实世界中的关键信息的情况下以清晰可读的方式放置标签可能是一个具有挑战性的问题。本文介绍了一种用于街景场景中的 AR 标签放置技术。我们提出了一种语义感知任务特定标签放置方法，通过一种新颖的特征图识别潜在的重要图像区域，我们将其称为指导图。给定输入图像，其显着性信息、语义信息和特定任务的重要性先验被集成到我们标记任务的指导图中。为了先学习任务，我们创建了一个带有用户标签偏好的标签放置数据集，并将其用于评估。我们的解决方案对优化问题中放置标签的约束进行编码以获得最终的标签布局，并将标签放置在适当的位置，以减少在街景 AR 场景中覆盖重要现实世界对象的机会。实验验证清楚地表明，我们的方法在 AR 街景导航和类似应用中优于以前的解决方案。