基于GPU的表面基于 gaze 映射用于XR体验
GPU accelerated surface-based gaze mapping for XR experiences
AI总结：
本文提出了一种基于GPU的算法，用于实时处理6自由度下的视觉注意力映射，提升XR体验中的交互效率和准确性。
摘要：
扩展现实是一个快速增长的领域，需要越来越多地分析和理解用户行为。特别是，在沉浸式体验中理解人类视觉注意力对于许多应用至关重要。通过从眼动追踪数据中构建注视密度图来可视化和分析视觉注意力。这种视觉注意力映射在3自由度（3DoF）体验（即，涉及360图像或视频）中已被很好地掌握，但在6自由度（6DoF）数据中则要少得多，当用户可以在3D空间中自由移动时。在这种情况下，视觉注意力信息必须映射到3D对象本身。一些解决方案用于构建此类表面基于的6DoF注意力图，但它们有若干缺点：处理时间、对网格分辨率和/或纹理映射的强依赖性，以及/或不实际的数据表示用于进一步处理。在此背景下，我们提出了一种新的基于GPU的算法，解决了上述问题，同时在交互时间内生成并在实时中渲染。在具有挑战性的场景中的实验展示了我们方法的准确性和鲁棒性。为了促进该领域的研究，源代码已公开发布，并整合到PLUME中，以便于在XR实验中使用。
英文摘要：
Extended reality is a fast-growing domain for which there is an increasing need to analyze and understand user behavior. In particular, understanding human visual attention during immersive experiences is crucial for many applications. The visualization and analysis of visual attention are commonly done by building fixation density maps from eye-tracking data. Such visual attention mapping is well mastered for 3 degrees of freedom (3DoF) experiences (\textit{i.e.}, involving 360 images or videos) but much less so for 6DoFs data, when the user can move freely in the 3D space. In that case, the visual attention information has to be mapped onto the 3D objects themselves. Some solutions exist for constructing such surface-based 6DoFs attention maps, however, they own several drawbacks: processing time, strong dependence on mesh resolution and/or texture mapping, and/or unpractical data representation for further processing. In this context, we propose a novel GPU-based algorithm that resolves the issues above while being generated in interactive time and rendered in real-time. Experiment on a challenging scene demonstrates the accuracy and robustness of our approach. To stimulate research in this area, the source code is publicly released and integrated into PLUME for ease of use in XR experiments.
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