A single-stage pixel-space diffusion model for direct 3D Gaussian Splat generation that bypasses latent compression and adds geometric supervisions to outperform prior multi-stage methods.
3d gaussian splatting as markov chain monte carlo
4 Pith papers cite this work. Polarity classification is still indexing.
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Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
RoDyGS separates static and dynamic elements in monocular videos using Gaussian splatting with regularization and introduces the Kubric-MRig benchmark for pose-free dynamic novel view synthesis.
Turbo-GS accelerates 3D Gaussian Splatting training via dilated rendering of pixel subsets, convergence-aware Gaussian budget allocation, and combined positional-appearance error densification to enable faster 4K fitting with preserved or improved rendering quality.
citing papers explorer
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PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation
A single-stage pixel-space diffusion model for direct 3D Gaussian Splat generation that bypasses latent compression and adds geometric supervisions to outperform prior multi-stage methods.
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Monte Carlo Energy Aggregation for Mobile 3D Gaussian Splatting
Flux-GS is a mobile-optimized 3D Gaussian Splatting method that compresses specular energy via Monte Carlo aggregation, recovers details with attribute-conditioned SH offsets, and uses multi-view guidance for densification to cut parameters while keeping visual quality.
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RoDyGS: Robust Dynamic Gaussian Splatting for Casual Videos
RoDyGS separates static and dynamic elements in monocular videos using Gaussian splatting with regularization and introduces the Kubric-MRig benchmark for pose-free dynamic novel view synthesis.
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Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields
Turbo-GS accelerates 3D Gaussian Splatting training via dilated rendering of pixel subsets, convergence-aware Gaussian budget allocation, and combined positional-appearance error densification to enable faster 4K fitting with preserved or improved rendering quality.