In this paper, we investigate how users perceive the visual quality of crowd character representations at different levels of detail (LoD) and viewing distances. Each representation: geometric meshes, image-based impostors, Neural Radiance Fields (NeRFs), and 3D Gaussians, exhibits distinct trade-offs between visual fidelity and computational performance. Our qualitative and quantitative results provide insights to guide the design of perceptually optimized LoD strategies for crowd rendering.

翻译：本文研究了用户在不同细节层次（LoD）和观察距离下对人群角色表征视觉质量的感知。每种表征形式——几何网格、基于图像的替代体、神经辐射场（NeRFs）以及3D高斯表征——均在视觉保真度与计算性能之间展现出不同的权衡特性。我们的定性与定量研究结果为设计感知优化的群体渲染LoD策略提供了指导依据。