StarPicker: A Technique for Selecting Dense Small Targets in AR-Based Data Visualization Environments

In the era of Big Data, augmented reality (AR)-based 3D visualization technology is gradually becoming an essential tool of effective information dissemination. However, 3D data visualization inevitably faces a series of challenges, particularly when visualizing large datasets within limited physical spaces. This often results in high spatial density, characterized by smaller target objects and occlusion of data objects, which greatly complicate object selection and subsequent interaction. To address these issues, this paper proposes a multimodal progressive target selection technique-StarPicker, which integrates the SpotLight metaphor and Clock metaphor. By leveraging wrist rotation and gesture interactions, StarPicker facilitates a coarse-to-fine semantic disambiguation and precise selection of target objects. To validate the effectiveness of StarPicker, a comparative user study was conducted against the state-of-the-art techniques (GridWall and FlowerCone) under high-density (up to 360 objects) and small-target (1cm) conditions. Experimental results demonstrate that StarPicker significantly outperforms the baselines in terms of target selection accuracy and user satisfaction, while achieving comparable or better completion times, especially in denser scenes. This work offers a novel approach to target selection and interaction technology in the field of AR-based 3D Big Data visualization.