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.

翻译：扩展现实是一个快速发展的领域，对其用户行为进行分析和理解的需求日益增长。特别是在沉浸式体验中理解人类的视觉注意力，对许多应用至关重要。视觉注意力的可视化与分析通常通过眼动追踪数据构建注视密度图来实现。这种视觉注意力映射方法在3自由度体验（即涉及360度图像或视频）中已较为成熟，但对于6自由度数据——当用户可在三维空间中自由移动时——则远未完善。在此情况下，视觉注意力信息必须映射到三维物体本身。现有构建此类基于表面的6自由度注意力图的解决方案存在若干缺陷：处理时间过长、对网格分辨率和/或纹理映射的强依赖性、和/或不利于后续处理的数据表示形式。为此，我们提出一种新颖的基于GPU的算法，该算法在交互时间内生成并实时渲染，同时解决了上述问题。在复杂场景上的实验证明了我们方法的准确性与鲁棒性。为促进该领域研究，源代码已公开发布并集成至PLUME平台，以便于在XR实验中使用。