FixingGS: Enhancing 3D Gaussian Splatting via Training-Free Score Distillation

Zhaorui Wang, Yi Gu, Deming Zhou, Renjing Xu

Keywords: Gaussian splatting, novel view synthesis, 3D reconstruction, Diffusion models

TL;DR: We propose FixingGS, a training-free framework that leverages diffusion-based distillation to significantly improve sparse-view 3D reconstruction with superior visual quality and cross-view consistency.

Abstract: Recently, 3D Gaussian Splatting (3DGS) has demonstrated remarkable success in 3D reconstruction and novel view synthesis. However, reconstructing 3D scenes from sparse viewpoints remains highly challenging due to insufficient visual information, which results in noticeable artifacts persisting across the 3D representation. To address this limitation, recent methods have resorted to generative priors to remove artifacts and complete missing content in under-constrained areas. Despite their effectiveness, these approaches struggle to ensure multi-view consistency, resulting in blurred structures and implausible details. In this work, we propose FixingGS, a training-free method that fully exploits the capabilities of the existing diffusion model for sparse-view 3DGS reconstruction enhancement. At the core of FixingGS is our distillation approach, which delivers more accurate and cross-view coherent diffusion priors, thereby enabling effective artifact removal and inpainting. In addition, we propose an adaptive progressive enhancement scheme that further refines reconstructions in under-constrained regions. Extensive experiments demonstrate that FixingGS surpasses existing state-of-the-art methods with superior visual quality and reconstruction performance. Our code will be released publicly.

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

Submission Number: 5473