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KoalaGS: Complexity-Aware Continuous Level of Detail for Gaussian Splatting

Paper recorded by Signals 4 on 2026-10-20 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-20 on arXiv · recorded by Signals 4 on 2026-10-21

Category: cs.CV · 计算机视觉 · first seen 2026-10-21

Abstract

Deploying a trained 3D Gaussian Splatting scene under shrinking compute budgets requires one primitive set to remain effective when rendered at smaller prefixes. In this continuous level of detail (LoD) setting, the challenge is to decide which Gaussians should retain capacity as the budget decreases. A pilot study on 3DGS-MCMC reconstructions shows that image complexity, computed from the input v

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#209 most recent of 756 cs.CV papers we have recorded · ↑ newer: PsycheMME: A Psychology-Informed Benchmark for Social Understanding an · ↓ older: Learning the 4D World through Point Trajectory Forecasting
Cite this page: KoalaGS: Complexity-Aware Continuous Level of Detail for Gaussian Splatting: the #209 most recent of 756 cs.CV papers we have recorded (as of 2026-10-20). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/koalags-complexity-aware-continuous-level-of-detail-for-gaussian-splatting.html
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