GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors

• URL: http://arxiv.org/abs/2508.09667v1
• Date: Wed, 13 Aug 2025 09:56:28 GMT
• Title: GSFixer: Improving 3D Gaussian Splatting with Reference-Guided Video Diffusion Priors
• Authors: Xingyilang Yin, Qi Zhang, Jiahao Chang, Ying Feng, Qingnan Fan, Xi Yang, Chi-Man Pun, Huaqi Zhang, Xiaodong Cun,
• Abstract summary: GSFixer is a framework designed to improve the quality of 3DGS representations reconstructed from sparse inputs.<n>Our model integrates both 2D semantic features and 3D geometric features of reference views extracted from the visual geometry foundation model.<n>Considering the lack of suitable benchmarks for 3DGS artifact restoration evaluation, we present DL3DV-Res which contains artifact frames rendered using low-quality 3DGS.
• Score: 44.901133648775605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reconstructing 3D scenes using 3D Gaussian Splatting (3DGS) from sparse views is an ill-posed problem due to insufficient information, often resulting in noticeable artifacts. While recent approaches have sought to leverage generative priors to complete information for under-constrained regions, they struggle to generate content that remains consistent with input observations. To address this challenge, we propose GSFixer, a novel framework designed to improve the quality of 3DGS representations reconstructed from sparse inputs. The core of our approach is the reference-guided video restoration model, built upon a DiT-based video diffusion model trained on paired artifact 3DGS renders and clean frames with additional reference-based conditions. Considering the input sparse views as references, our model integrates both 2D semantic features and 3D geometric features of reference views extracted from the visual geometry foundation model, enhancing the semantic coherence and 3D consistency when fixing artifact novel views. Furthermore, considering the lack of suitable benchmarks for 3DGS artifact restoration evaluation, we present DL3DV-Res which contains artifact frames rendered using low-quality 3DGS. Extensive experiments demonstrate our GSFixer outperforms current state-of-the-art methods in 3DGS artifact restoration and sparse-view 3D reconstruction. Project page: https://github.com/GVCLab/GSFixer.

Related papers

• Perceptual Quality Assessment of 3D Gaussian Splatting: A Subjective Dataset and Prediction Metric [76.66966098297986]
  We present 3DGS-QA, the first subjective quality assessment dataset for 3DGS.<n>It comprises 225 degraded reconstructions across 15 object types, enabling a controlled investigation of common distortion factors.<n>Our model extracts spatial and photometric cues from the Gaussian representation to estimate perceived quality in a structure-aware manner.
  arXiv  Detail & Related papers  (2025-11-11T09:34:20Z)
• DIP-GS: Deep Image Prior For Gaussian Splatting Sparse View Recovery [31.43307762723943]
  3D Gaussian Splatting (3DGS) is a leading 3D scene reconstruction method, obtaining high-quality reconstruction with real-time rendering performance.<n>While achieving superior performance in the presence of many views, 3DGS struggles with sparse view reconstruction, where the input views are sparse and do not fully cover the scene and have low overlaps.<n>In this paper, we propose DIP-GS, a Deep Image Prior (DIP) 3DGS representation.
  arXiv  Detail & Related papers  (2025-08-10T14:47:32Z)
• ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors [37.455535904703204]
  We propose a 3DGS-based pipeline that generates additional training views to enhance reconstruction.<n>Fine-tuning 3D Gaussians with these enhanced views significantly improves reconstruction quality.<n> Experiments demonstrate that our approach outperforms existing 3DGS-based methods.
  arXiv  Detail & Related papers  (2025-08-08T05:01:17Z)
• T-3DGS: Removing Transient Objects for 3D Scene Reconstruction [83.05271859398779]
  Transient objects in video sequences can significantly degrade the quality of 3D scene reconstructions.<n>We propose T-3DGS, a novel framework that robustly filters out transient distractors during 3D reconstruction using Gaussian Splatting.
  arXiv  Detail & Related papers  (2024-11-29T07:45:24Z)
• PF3plat: Pose-Free Feed-Forward 3D Gaussian Splatting [54.7468067660037]
  PF3plat sets a new state-of-the-art across all benchmarks, supported by comprehensive ablation studies validating our design choices.<n>Our framework capitalizes on fast speed, scalability, and high-quality 3D reconstruction and view synthesis capabilities of 3DGS.
  arXiv  Detail & Related papers  (2024-10-29T15:28:15Z)
• 3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors [13.191199172286508]
  Novel-view synthesis aims to generate novel views of a scene from multiple input images or videos. 3DGS-Enhancer is a novel pipeline for enhancing the representation quality of 3DGS representations.
  arXiv  Detail & Related papers  (2024-10-21T17:59:09Z)
• GSD: View-Guided Gaussian Splatting Diffusion for 3D Reconstruction [52.04103235260539]
  We present a diffusion model approach based on Gaussian Splatting representation for 3D object reconstruction from a single view. The model learns to generate 3D objects represented by sets of GS ellipsoids. The final reconstructed objects explicitly come with high-quality 3D structure and texture, and can be efficiently rendered in arbitrary views.
  arXiv  Detail & Related papers  (2024-07-05T03:43:08Z)
• SAGS: Structure-Aware 3D Gaussian Splatting [53.6730827668389]
  We propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene. SAGS reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets.
  arXiv  Detail & Related papers  (2024-04-29T23:26:30Z)
• SparseGS: Real-Time 360° Sparse View Synthesis using Gaussian Splatting [6.506706621221143]
  3D Splatting (3DGS) has recently enabled real-time rendering of 3D scenes for novel view synthesis.<n>This technique requires dense training views to accurately reconstruct 3D geometry.<n>We introduce SparseGS, an efficient training pipeline designed to address the limitations of 3DGS in scenarios with sparse training views.
  arXiv  Detail & Related papers  (2023-11-30T21:38:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.