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SuperGS: Super-Resolution 3D Gaussian Splatting Enhanced by Variational Residual Features and Uncertainty-Augmented Learning

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arxiv 2410.02571 v3 pith:C6GADCNR submitted 2024-10-03 cs.CV

Shiyun Xie , Zhiru Wang , Xu Wang , Yinghao Zhu , Chengwei Pan , Xiwang Dong This is my paper
classification cs.CV
keywords supergslow-resolutionsuper-resolutionfeaturesframeworkgaussianhigh-resolutionhrnvs
verification ladder T0 review T1 audit T2 compute T3 formal
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Recently, 3D Gaussian Splatting (3DGS) has exceled in novel view synthesis (NVS) with its real-time rendering capabilities and superior quality. However, it faces challenges for high-resolution novel view synthesis (HRNVS) due to the coarse nature of primitives derived from low-resolution input views. To address this issue, we propose Super-Resolution 3DGS (SuperGS), which is an expansion of 3DGS designed with a two-stage coarse-to-fine training framework. In this framework, we use a latent feature field to represent the low-resolution scene, serving as both the initialization and foundational information for super-resolution optimization. Additionally, we introduce variational residual features to enhance high-resolution details, using their variance as uncertainty estimates to guide the densification process and loss computation. Furthermore, the introduction of a multi-view joint learning approach helps mitigate ambiguities caused by multi-view inconsistencies in the pseudo labels. Extensive experiments demonstrate that SuperGS surpasses state-of-the-art HRNVS methods on both real-world and synthetic datasets using only low-resolution inputs. Code is available at https://github.com/SYXieee/SuperGS.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SubSplat: High-Resolution Pixel-aligned 3DGS via Sub-pixel Gaussian Reparameterization

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A feed-forward Gaussian-splatting model that subdivides each primary Gaussian into learned sub-pixel primitives, achieving state-of-the-art high-resolution novel-view synthesis from low-resolution inputs.

  2. MACRO: Training-free Multi-plane Attention for Closeup Render Optimization

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Training-free multi-plane attention with image-space scale-matched reference crops restores correct close-up detail from 3DGS without retraining the enhancer.

  3. R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    The survey formalizes degradation-aware rendering for 3D Low-Level Vision and organizes roughly 100 methods on super-resolution, deblurring, weather removal, restoration, and enhancement in NeRF and 3DGS pipelines.

  4. SplatPainter: Interactive Authoring of 3D Gaussians from 2D Edits via Test-Time Training

    cs.CV 2025-12 conditional novelty 5.0 of 10

    A test-time-trained feedforward model that propagates 2D edits onto 3D Gaussian attributes at interactive speeds.

  5. GaussianVAE: Adaptive Learning Dynamics of 3D Gaussians for High-Fidelity Super-Resolution

    cs.GR 2025-06 reject novelty 5.0 of 10

    A VAE with transformer attention and Hessian-guided sampling is proposed to extrapolate 3D Gaussian Splatting scenes beyond their training resolution, claiming 0.015s inference and improved Chamfer distance and Censeo...

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