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3D Gaussian Splatting as Markov Chain Monte Carlo
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While 3D Gaussian Splatting has recently become popular for neural rendering, current methods rely on carefully engineered cloning and splitting strategies for placing Gaussians, which can lead to poor-quality renderings, and reliance on a good initialization. In this work, we rethink the set of 3D Gaussians as a random sample drawn from an underlying probability distribution describing the physical representation of the scene-in other words, Markov Chain Monte Carlo (MCMC) samples. Under this view, we show that the 3D Gaussian updates can be converted as Stochastic Gradient Langevin Dynamics (SGLD) updates by simply introducing noise. We then rewrite the densification and pruning strategies in 3D Gaussian Splatting as simply a deterministic state transition of MCMC samples, removing these heuristics from the framework. To do so, we revise the 'cloning' of Gaussians into a relocalization scheme that approximately preserves sample probability. To encourage efficient use of Gaussians, we introduce a regularizer that promotes the removal of unused Gaussians. On various standard evaluation scenes, we show that our method provides improved rendering quality, easy control over the number of Gaussians, and robustness to initialization.
Forward citations
Cited by 6 Pith papers
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ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
ContraGS trains 3D Gaussian Splatting directly on codebook-compressed representations, cutting peak model memory ~3.5x with small quality loss.
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SplatCtrl: Perception-Action Coupling via Gaussian Scene Representations and Reactive Robot Control
SplatCtrl couples real-time isotropic Gaussian scene reconstruction from RGB-D with continuous GPDF-derived SDFs inside control-barrier QP-IK for collision-free 6-DoF robot motion in dynamic environments.
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PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation
PixGS is a single-stage pixel-space diffusion model that directly produces high-quality 3D Gaussian Splats from text or images in ~1s, outperforming multi-stage latent methods on standard benchmarks.
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CF3: Compact and Fast 3D Feature Fields
CF3 builds a compact 3D feature field from a pre-trained 3DGS by feature lifting, per-Gaussian autoencoding, and adaptive sparsification, matching baseline segmentation quality with roughly 5% of the Gaussians.
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3D Scene-Camera Representation with Joint Camera Photometric Optimization
A 3D Gaussian splatting pipeline jointly optimizes a per-pixel photometric camera MLP and the scene radiance, removing vignetting, dirt, and defocus during novel-view synthesis.
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GSCodec Studio: A Modular Framework for Gaussian Splat Compression
GSCodec Studio is a modular open-source framework for Gaussian Splat compression, and its composed Static and Dynamic GSCodec pipelines report competitive rate-distortion results against several baselines.
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