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

Key points

• In this continuous level of detail (LoD) setting, the challenge is to decide which Gaussians should retain capacity as the budget decreases.1
• A pilot study on 3DGS-MCMC reconstructions shows that image complexity, computed from the input views before optimization, predicts where reconstruction error concentrates in image and 3D space.1
• A primitive importance score then enables inference at low-capacity, while preserving the scene structure.1
• Tested across three datasets, KoalaGS maintains competitive full-capacity quality while significantly improving quality across the budget spectrum, yielding smoother transitions across LoDs, and enabling faster progressive streaming.1

Sources (1)

• [1]KoalaGS: Complexity-Aware Continuous Level of Detail for Gaussian Splatting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 05:55 PM

  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.

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