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.
Abstract (translated)
最近，3D高斯点阵（3D Gaussian Splatting, 3DGS）在三维重建和新视角合成方面取得了显著的成功。然而，由于视觉信息不足，从稀疏视点重建三维场景仍然极具挑战性，这导致明显的伪影持续存在于三维表示中。为了解决这一限制，最近的方法转向生成先验来移除这些伪影并在约束条件不足的区域补充缺失内容。尽管这些方法效果显著，但它们难以确保多视角一致性，从而导致结构模糊和细节不合理的问题。 在本工作中，我们提出了FixingGS，这是一种无需训练的方法，充分利用现有的扩散模型来进行稀疏视点3DGS重建增强。FixingGS的核心是我们的知识蒸馏方法，这种方法提供了更准确且跨视角一致的扩散先验，从而使有效的伪影移除和修复成为可能。此外，我们还提出了一种自适应渐进式增强方案，进一步细化在约束条件不足区域中的重建结果。 大量的实验表明，FixingGS在视觉质量和重建性能方面都超越了现有的最先进方法。我们的代码将公开发布。
URL
https://arxiv.org/abs/2509.18759
