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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 18 Pith papers
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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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SCINeRF and SCISplat recover a view-consistent 3D scene representation from a single snapshot compressive image, with SCISplat reaching 35.94 dB PSNR and 205 FPS rendering.
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Replacing EWA splatting's linearized projection with an Unscented Transform lets 3DGS handle fisheye and rolling-shutter cameras and enables hybrid rasterization plus traced secondary rays.
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Generative Densification: Learning to Densify Gaussians for High-Fidelity Generalizable 3D Reconstruction
Generative Densification improves feed-forward Gaussian 3D reconstruction by learning to generate fine Gaussians for detailed regions in one forward pass, and it beats baselines on object and scene datasets.
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Steepest Descent Density Control for Compact 3D Gaussian Splatting
SteepGS splits a 3D Gaussian only when a computed splitting matrix has a negative eigenvalue, placing two half-opacity offspring along the steepest descent direction, achieving about 50% point reduction with comparabl...
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A Gaussian splatting pipeline reconstructs indoor scenes as separable objects and uses a trained completion model to fill in occluded surfaces zero-shot.
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