mlsanityData to 7 Oct 2026

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

arXiv 2510.20558 ↗First posted 23 Oct 2025cs.CV cs.GR cs.HCcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 23 Oct 2025.

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References · 9

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,141
  3. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,698
  4. 20234D Gaussian Splatting for Real-Time Dynamic Scene Renderingcited by 1,144
  5. 2021PlenOctrees for Real-time Rendering of Neural Radiance Fieldscited by 904
  6. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 661
  7. 2024LP-3DGS: Learning to Prune 3D Gaussian Splattingcited by 53
  8. 2025CrowdSplat: Exploring Gaussian Splatting For Crowd Renderingcited by 2
  9. 2025Evaluating CrowdSplat: Perceived Level of Detail for Gaussian Crowdscited by 1

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