TokenGS uses learnable Gaussian tokens in an encoder-decoder architecture to regress 3D means directly, achieving SOTA feed-forward reconstruction on static and dynamic scenes with better robustness.
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Mip-splatting: Alias-free 3d gaussian splat- ting
11 Pith papers cite this work. Polarity classification is still indexing.
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AnchorSplat uses anchor-aligned 3D Gaussians guided by geometric priors for feed-forward scene reconstruction, achieving SOTA novel view synthesis on ScanNet++ with fewer primitives and better view consistency.
ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.
RDSplat is the first 3D Gaussian Splatting watermarking method that maintains 0.701 bit accuracy against both 2D and 3D diffusion editing by embedding only in low-frequency primitives selected via FAPS.
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.
A Z-order transformer organizes unstructured Gaussians for sparse attention, enabling feed-forward prediction of high-quality 3D splats with fewer primitives.
DualSplat bootstraps object-level pseudo-masks from initial 3DGS reconstruction failures using residuals and SAM2 to enable robust second-pass optimization in transient-heavy scenes.
A dynamic training framework for 3D Gaussian Splatting alternates incremental pruning and adaptive growing of primitives to maintain high rendering quality at up to 80% lower peak memory than standard 3DGS.
PDF-GS progressively filters distractors in 3D Gaussian Splatting by exploiting the method's self-suppression of inconsistent signals, yielding high-fidelity distractor-free 3D models without changing the base architecture or adding inference cost.
Splatography improves dynamic 3D reconstruction from sparse multi-view videos by splitting foreground and background Gaussian representations and applying tailored deformation learning for each.
GaussianZoom enables high-fidelity extreme zoom-in 3D rendering from low-res inputs via an iterative framework combining geometry-consistent modeling, depth-based super-resolution, VLM detail synthesis, and an expandable continuous Level-of-Detail hierarchy.
citing papers explorer
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TokenGS: Decoupling 3D Gaussian Prediction from Pixels with Learnable Tokens
TokenGS uses learnable Gaussian tokens in an encoder-decoder architecture to regress 3D means directly, achieving SOTA feed-forward reconstruction on static and dynamic scenes with better robustness.
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AnchorSplat: Feed-Forward 3D Gaussian Splatting with 3D Geometric Priors
AnchorSplat uses anchor-aligned 3D Gaussians guided by geometric priors for feed-forward scene reconstruction, achieving SOTA novel view synthesis on ScanNet++ with fewer primitives and better view consistency.
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ProDiG: Progressive Diffusion-Guided Gaussian Splatting for Aerial to Ground Reconstruction
ProDiG progressively transforms aerial Gaussian splats into coherent ground-level 3D reconstructions via diffusion guidance and specialized attention modules.
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RDSplat: Robust Watermarking for 3D Gaussian Splatting Against 2D and 3D Diffusion Editing
RDSplat is the first 3D Gaussian Splatting watermarking method that maintains 0.701 bit accuracy against both 2D and 3D diffusion editing by embedding only in low-frequency primitives selected via FAPS.
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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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Z-Order Transformer for Feed-Forward Gaussian Splatting
A Z-order transformer organizes unstructured Gaussians for sparse attention, enabling feed-forward prediction of high-quality 3D splats with fewer primitives.
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DualSplat: Robust 3D Gaussian Splatting via Pseudo-Mask Bootstrapping from Reconstruction Failures
DualSplat bootstraps object-level pseudo-masks from initial 3DGS reconstruction failures using residuals and SAM2 to enable robust second-pass optimization in transient-heavy scenes.
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Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training
A dynamic training framework for 3D Gaussian Splatting alternates incremental pruning and adaptive growing of primitives to maintain high rendering quality at up to 80% lower peak memory than standard 3DGS.
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PDF-GS: Progressive Distractor Filtering for Robust 3D Gaussian Splatting
PDF-GS progressively filters distractors in 3D Gaussian Splatting by exploiting the method's self-suppression of inconsistent signals, yielding high-fidelity distractor-free 3D models without changing the base architecture or adding inference cost.
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Splatography: Sparse multi-view dynamic Gaussian Splatting for filmmaking challenges
Splatography improves dynamic 3D reconstruction from sparse multi-view videos by splitting foreground and background Gaussian representations and applying tailored deformation learning for each.
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GaussianZoom: Progressive Zoom-in Generative 3D Gaussian Splatting with Geometric and Semantic Guidance
GaussianZoom enables high-fidelity extreme zoom-in 3D rendering from low-res inputs via an iterative framework combining geometry-consistent modeling, depth-based super-resolution, VLM detail synthesis, and an expandable continuous Level-of-Detail hierarchy.