VidSplat: Gaussian Splatting Reconstruction with Geometry-Guided Video Diffusion Priors
- URL: http://arxiv.org/abs/2605.11424v1
- Date: Tue, 12 May 2026 02:20:31 GMT
- Title: VidSplat: Gaussian Splatting Reconstruction with Geometry-Guided Video Diffusion Priors
- Abstract summary: VidSplat is a training-free generative reconstruction framework.<n>We tackle two key challenges that enable the effective integration of generation and reconstruction.<n>Experiments on widely used benchmarks demonstrate our superior performance in sparse-view scene reconstruction.
- Score: 79.88300861391114
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Gaussian Splatting has achieved remarkable progress in multi-view surface reconstruction, yet it exhibits notable degradation when only few views are available. Although recent efforts alleviate this issue by enhancing multi-view consistency to produce plausible surfaces, they struggle to infer unseen, occluded, or weakly constrained regions beyond the input coverage. To address this limitation, we present VidSplat, a training-free generative reconstruction framework that leverages powerful video diffusion priors to iteratively synthesize novel views that compensate for missing input coverage, and thereby recover complete 3D scenes from sparse inputs. Specifically, we tackle two key challenges that enable the effective integration of generation and reconstruction. First, for 3D consistent generation, we elaborate a training-free, stage-wise denoising strategy that adaptively guides the denoising direction toward the underlying geometry using the rendered RGB and mask images. Second, to enhance the reconstruction, we develop an iterative mechanism that samples camera trajectories, explores unobserved regions, synthesizes novel views, and supplements training through confidence weighted refinement. VidSplat performs robustly to sparse input and even a single image. Extensive experiments on widely used benchmarks demonstrate our superior performance in sparse-view scene reconstruction.
Related papers
- GaussVid: Sparse-View Gaussian Splatting with 3D-Aware Video Diffusion Priors [14.045684695919116]
3DGS has achieved remarkable success in synthesis; however, reconstructions under sparse views often exhibit noticeable artifacts.<n>We propose a novel 3D-aware video restoration framework designed to enhance the quality of sparse 3DGS reconstruction.<n>Our approach attains the best pixel- and structure-level fidelity (PSNR/SSIM) and improves multi-view consistency.
arXiv Detail & Related papers (2026-08-22T08:47:29Z) - Rewis3d: Reconstruction Improves Weakly-Supervised Semantic Segmentation [58.37682525044409]
Rewis3d is a framework that leverages recent advances in feed-forward 3D reconstruction to significantly improve weakly supervised semantic segmentation on 2D images.<n>We show that Rewis3d achieves state-of-the-art performance in sparse supervision, outperforming existing approaches by 2-7% without requiring additional labels or inference overhead.
arXiv Detail & Related papers (2026-03-06T15:25:20Z) - Pseudo-View Enhancement via Confidence Fusion for Unposed Sparse-View Reconstruction [7.8268279161630785]
3D scene reconstruction under unposed sparse viewpoints is a highly challenging yet practically important problem.<n>We propose a novel framework for sparse-view outdoor reconstruction that achieves high-quality results through bidirectional pseudo frame restoration.<n>Designs significantly enhance reconstruction completeness, suppress floating artifacts and improve overall geometric consistency under extreme view sparsity.
arXiv Detail & Related papers (2026-02-25T03:45:47Z) - G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior [53.762256749551284]
We identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction.<n>We incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models.<n>Our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios.
arXiv Detail & Related papers (2025-10-14T03:06:28Z) - SpatialCrafter: Unleashing the Imagination of Video Diffusion Models for Scene Reconstruction from Limited Observations [44.53106180688135]
This work takes on the challenge of reconstructing 3D scenes from sparse or single-view inputs.<n>We introduce SpatialCrafter, a framework that leverages the rich knowledge in video diffusion models to generate plausible additional observations.<n>Through a trainable camera encoder and an epipolar attention mechanism for explicit geometric constraints, we achieve precise camera control and 3D consistency.
arXiv Detail & Related papers (2025-05-17T13:05:13Z) - GenFusion: Closing the Loop between Reconstruction and Generation via Videos [24.195304481751602]
We propose a reconstruction-driven video diffusion model that learns to condition video frames on artifact-prone RGB-D renderings.<n>We also propose a cyclical fusion pipeline that iteratively adds restoration frames from the generative model to the training set.
arXiv Detail & Related papers (2025-03-27T07:16:24Z) - Decompositional Neural Scene Reconstruction with Generative Diffusion Prior [64.71091831762214]
Decompositional reconstruction of 3D scenes, with complete shapes and detailed texture, is intriguing for downstream applications.<n>Recent approaches incorporate semantic or geometric regularization to address this issue, but they suffer significant degradation in underconstrained areas.<n>We propose DP-Recon, which employs diffusion priors in the form of Score Distillation Sampling (SDS) to optimize the neural representation of each individual object under novel views.
arXiv Detail & Related papers (2025-03-19T02:11:31Z) - MuDG: Taming Multi-modal Diffusion with Gaussian Splatting for Urban Scene Reconstruction [44.592566642185425]
MuDG is an innovative framework that integrates Multi-modal Diffusion model with Gaussian Splatting (GS) for Urban Scene Reconstruction.<n>We show that MuDG outperforms existing methods in both reconstruction and photorealistic synthesis quality.
arXiv Detail & Related papers (2025-03-13T17:48:41Z) - DGTR: Distributed Gaussian Turbo-Reconstruction for Sparse-View Vast Scenes [81.56206845824572]
Novel-view synthesis (NVS) approaches play a critical role in vast scene reconstruction.
Few-shot methods often struggle with poor reconstruction quality in vast environments.
This paper presents DGTR, a novel distributed framework for efficient Gaussian reconstruction for sparse-view vast scenes.
arXiv Detail & Related papers (2024-11-19T07:51:44Z) - PVP-Recon: Progressive View Planning via Warping Consistency for Sparse-View Surface Reconstruction [49.7580491592023]
We propose PVP-Recon, a novel and effective sparse-view surface reconstruction method.
PVP-Recon starts initial surface reconstruction with as few as 3 views and progressively adds new views.
This progressive view planning progress is interleaved with a neural SDF-based reconstruction module.
arXiv Detail & Related papers (2024-09-09T10:06:34Z) - DiViNeT: 3D Reconstruction from Disparate Views via Neural Template
Regularization [7.488962492863031]
We present a volume rendering-based neural surface reconstruction method that takes as few as three disparate RGB images as input.
Our key idea is to regularize the reconstruction, which is severely ill-posed and leaving significant gaps between the sparse views.
Our approach achieves the best reconstruction quality among existing methods in the presence of such sparse views.
arXiv Detail & Related papers (2023-06-07T18:05:14Z)
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.