arXiv Computer Vision
RobustGS: Unified Boosting of Feedforward 3D Gaussian Splatting under Low-Quality Conditions
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ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models
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DensiTok: Making Feed-Forward 3D Gaussian Splatting See More Views Than It Is Given
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Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
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SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting
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arXiv Computer Vision
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NOVA-GS: Noise-Aware View-Consistent Gaussian Splatting for Low-Light Novel View Synthesis
arXiv:2609.12682v1 Announce Type: new Abstract: Reconstructing 3D scenes under real-world low-light conditions remains challenging due to severe sensor noise, low signal-to-noise ratios, and degraded...
arXiv Computer Vision
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Filling the Unseen: Scene Extrapolation via 3D Gaussian Splatting
arXiv:2609.13262v1 Announce Type: new Abstract: 3D Gaussian Splatting achieves photorealistic reconstruction within training view distribution, yet it degrades on out-of-distribution novel views, exh...