研究不同细节层级下人群视觉表现的感知质量，指导渲染优化。
From Far and Near: Perceptual Evaluation of Crowd Representations Across Levels of Detail
- 对比网格、图像代理、NeRF和3D高斯等表示方法的视觉与计算权衡
- 发现远距离时低细节表示仍具感知保真度，可大幅降低计算开销
- 适合游戏/影视中大规模人群渲染的细节层次策略设计
本文研究用户在不同细节层级（LoD）和观看距离下对人群角色视觉质量的感知。每种表示方式——包括几何网格、基于图像的代理、神经辐射场（NeRF）和3D高斯——在视觉保真度与计算性能之间表现出不同权衡。通过定性和定量分析，我们获得有助于设计感知优化的多级细节策略的洞见，为大规模人群渲染提供指导。
原文摘要 · Abstract (English)
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, including 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.
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