From Far and Near: Perceptual Evaluation of Crowd Representations Across Levels of Detail

TL;DR

This paper explores how different crowd representation methods are perceived at various levels of detail and distances, providing insights for designing perceptually optimized crowd rendering techniques.

Contribution

It introduces a comprehensive evaluation of multiple crowd representation methods across levels of detail and viewing distances, guiding perceptually optimized rendering strategies.

Findings

Neural Radiance Fields offer high visual fidelity at close distances.

Image-based impostors are computationally efficient at far distances.

Different representations have distinct trade-offs between quality and performance.

Abstract

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.