HAD uses multi-view reasoning from a pre-trained feedforward NVS network to estimate and mask hallucination scores in diffusion priors, reducing artifacts and achieving SOTA novel view synthesis in sparse-view 3D reconstruction.
Tensorf: Tensorial radiance fields
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MetroGS combines distributed 2D Gaussian Splatting with structured dense enhancement, progressive hybrid optimization, and depth-guided appearance modeling to deliver higher geometric accuracy and stability in large-scale urban reconstruction.
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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HAD: Hallucination-Aware Diffusion Priors for 3D Reconstruction
HAD uses multi-view reasoning from a pre-trained feedforward NVS network to estimate and mask hallucination scores in diffusion priors, reducing artifacts and achieving SOTA novel view synthesis in sparse-view 3D reconstruction.
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MetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes
MetroGS combines distributed 2D Gaussian Splatting with structured dense enhancement, progressive hybrid optimization, and depth-guided appearance modeling to deliver higher geometric accuracy and stability in large-scale urban reconstruction.
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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.