Eulerian Gaussian Splatting using Hashed Probability Pyramids
Quick Answer
This paper shows that The new Eulerian Gaussian Splatting framework enhances 3D Gaussian Splatting by utilizing gradient-based optimization of a learnable density, achieving state-of-the-art reconstruction quality on mip-NeRF 360 while maintaining high rendering speeds.
Quick Take
The new Eulerian Gaussian Splatting framework enhances 3D Gaussian Splatting by utilizing gradient-based optimization of a learnable density, achieving state-of-the-art reconstruction quality on mip-NeRF 360 while maintaining high rendering speeds. This method eliminates the need for heuristic manipulation of primitives, allowing for more robust and efficient volume exploration.
Key Points
- Introduces a probabilistic splat-based radiance field framework for 3D Gaussian Splatting.
- Utilizes a memory-efficient multi-scale hierarchical grid for gradient-based optimization.
- Achieves state-of-the-art reconstruction quality on mip-NeRF 360.
- Eliminates brittle priors by allowing probability mass to flow where needed.
- Reduces optimization variance with an unbiased gradient estimator using control variates.
Paper Resources
Article Excerpt
From source RSS / original summaryarXiv:2605. 29136v1 Announce Type: new Abstract: We introduce a probabilistic splat-based radiance field framework that retains the fast rasterization and test-time efficiency of 3D Gaussian Splatting (3DGS) while replacing heuristic primitive manipulation with gradient-based optimization of a volumetric probability density. Rather than relocating, splitting, or culling Gaussians via hand-tuned densification (e. g. , ADC), we treat primitive locations as samples drawn from a persistent, learnable density.
We instantiate this density using a novel, memory-efficient multi-scale hierarchical grid that enables end-to-end gradient-based optimization. To stabilize the optimization, we derive an unbiased gradient estimator with control variates that markedly reduces variance. By allowing probability mass to flow to where the loss demands, our framework eliminates brittle priors and naturally explores the volume, achieving state-of-the-art reconstruction quality on mip-NeRF 360 while preserving 3DGS-level rendering speed.
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