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ReSplat: Degradation-agnostic Feed-forward Gaussian Splatting via Self-guided Residual Diffusion

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 5.5Download Report PDF
Universal Image Restoration
Abstract:

Recent advances in novel view synthesis (NVS) have predominantly focused on ideal, clear input settings, limiting their applicability in real-world environments with common degradations such as blur, low-light, haze, rain, and snow. While some approaches address NVS under specific degradation types, they are often tailored to narrow cases, lacking the generalizability needed for broader scenarios. To address this issue, we propose Restoration-based feed-forward Gaussian Splatting, named ReSplat, a novel framework capable of handling degraded multi-view inputs. Our model jointly estimates restored images and gaussians to represent the clear scene for NVS. We enable multi-view consistent universal image restoration by utilizing the 3d gaussians generated during the diffusion sampling process as self-guidance. This results in sharper and more reliable novel views. Notably, our framework adapts to various degradations without prior knowledge of their specific types. Extensive experiments demonstrate that ReSplat significantly outperforms existing methods across challenging conditions, including blur, low-light, haze, rain, and snow, delivering superior visual quality and robust NVS performance.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces ReSplat, a feed-forward framework for degradation-agnostic novel view synthesis using Gaussian Splatting. It occupies the 'Feed-Forward Universal Restoration' leaf within the 'Degradation-Agnostic and Universal Frameworks' branch, where it is currently the only paper. This sparse positioning suggests the work addresses an underexplored niche: combining universal restoration with efficient feed-forward Gaussian Splatting, as opposed to degradation-specific methods (which populate sibling branches like 'Low-Light Gaussian Splatting' or 'Motion Blur Gaussian Splatting') or post-processing approaches.

The taxonomy reveals substantial neighboring work in degradation-specific directions. The 'Degradation-Specific Gaussian Splatting Methods' branch contains papers targeting low-light, motion blur, and quality enhancement separately, while 'Degradation-Specific Neural Radiance Field Methods' addresses similar problems in NeRF frameworks. The 'Post-Rendering Enhancement' leaf under the same parent branch represents an alternative strategy: restoring quality after initial rendering rather than jointly. ReSplat diverges by unifying restoration and synthesis in a single forward pass without prior knowledge of degradation type, contrasting with the specialized priors embedded in neighboring methods.

Among 29 candidates examined, none clearly refute the three core contributions. The ReSplat framework itself (10 candidates examined, 0 refutable) appears novel in its degradation-agnostic feed-forward design for Gaussian Splatting. The multi-view aligned denoising diffusion model with 3D cross-attention (10 candidates, 0 refutable) and the multi-view aligned pre-filtering module (9 candidates, 0 refutable) similarly show no direct overlap in the limited search. These statistics suggest the combination of techniques—feed-forward restoration, 3D-guided diffusion, and artifact-free filtering—may represent incremental integration rather than entirely unprecedented components, though the search scope precludes definitive conclusions.

Based on top-29 semantic matches, the work appears to occupy a genuinely sparse research direction, being the sole representative in its taxonomy leaf. However, the limited search scale means closely related work in diffusion-guided reconstruction or universal restoration frameworks outside the examined candidates could exist. The analysis covers semantic neighbors and citation-expanded papers but does not exhaustively survey all degradation-agnostic Gaussian Splatting literature, leaving open the possibility of overlooked precedents in less-cited or concurrent work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: novel view synthesis from degraded multi-view images. The field has evolved into a rich landscape organized around how methods handle image degradations during 3D reconstruction and rendering. At the highest level, the taxonomy distinguishes between degradation-specific approaches—tailored to particular artifacts like noise, blur, or low light in either Neural Radiance Fields or Gaussian Splatting frameworks—and degradation-agnostic or universal frameworks that aim to restore quality across multiple corruption types in a single pipeline. Additional branches address sparse-view scenarios with degradation, occlusion handling, diffusion-based generative models for multi-view consistency, human-centric synthesis, feature-based denoising, depth-driven methods, and specialized domains such as medical imaging. This structure reflects a tension between designing bespoke solutions for known degradations (e.g., Gaussian in Dark[12] for low-light scenes, WaterHE-NeRF[29] for underwater haze) and building more flexible systems that generalize across corruption types without retraining. Recent work has increasingly explored universal restoration strategies that decouple degradation removal from 3D representation learning. ReSplat[0] exemplifies this trend by proposing a feed-forward restoration module applicable to diverse input corruptions, contrasting with earlier degradation-specific methods like DP-NeRF[21] or RustNeRF[25] that embed particular priors into the radiance field itself. Meanwhile, diffusion-based approaches such as Latent Diffusion Splatting[7] and multi-view diffusion models leverage generative priors to handle severe degradations, though they often trade off computational cost for robustness. Sparse-view methods like SparseNeRF[5] and medical applications such as DentalSplat[3] further complicate the landscape by combining limited observations with noisy inputs. ReSplat[0] sits within the degradation-agnostic branch, emphasizing efficiency and generalization, and contrasts with works like Nerflix[4] or DeOcc[6] that target specific occlusion or artifact patterns, highlighting an ongoing debate between specialization and universality in handling real-world image quality challenges.

Claimed Contributions

ReSplat framework for degradation-agnostic feed-forward Gaussian Splatting

The authors introduce ReSplat, a framework that jointly estimates restored images and 3D Gaussians to handle degraded multi-view inputs for novel view synthesis. The framework adapts to various degradations (blur, low-light, haze, rain, snow) without requiring prior knowledge of degradation types.

10 retrieved papers
Multi-view aligned denoising diffusion model with 3D cross-attention

The authors propose a diffusion-based universal image restoration method that uses 3D cross-attention to leverage Gaussian centroids (3D geometry) as self-guidance during the diffusion sampling process, enabling multi-view consistent restoration.

10 retrieved papers
Multi-view aligned pre-filtering module for artifact-free novel view synthesis

The authors design a pre-filtering module that computes degradation-aware weight maps applied to image features before Gaussian ellipsoid generation. This process helps achieve artifact-free novel view synthesis by down-weighting regions with residual artifacts while preserving geometry-consistent structures.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ReSplat framework for degradation-agnostic feed-forward Gaussian Splatting

The authors introduce ReSplat, a framework that jointly estimates restored images and 3D Gaussians to handle degraded multi-view inputs for novel view synthesis. The framework adapts to various degradations (blur, low-light, haze, rain, snow) without requiring prior knowledge of degradation types.

Contribution

Multi-view aligned denoising diffusion model with 3D cross-attention

The authors propose a diffusion-based universal image restoration method that uses 3D cross-attention to leverage Gaussian centroids (3D geometry) as self-guidance during the diffusion sampling process, enabling multi-view consistent restoration.

Contribution

Multi-view aligned pre-filtering module for artifact-free novel view synthesis

The authors design a pre-filtering module that computes degradation-aware weight maps applied to image features before Gaussian ellipsoid generation. This process helps achieve artifact-free novel view synthesis by down-weighting regions with residual artifacts while preserving geometry-consistent structures.

Table of Contents

ReSplat: Degradation-agnostic Feed-forward Gaussian Splatting via Self-guided Residual Diffusion | Novelty Validation