Abstract
Gaussian Splatting (GS), a recent technique for converting discrete points into continuous spatial representations, has shown promising results in 3D scene modeling and 2D image super-resolution. In this paper, we explore its untapped potential for image inpainting, which demands both locally coherent pixel synthesis and globally consistent semantic restoration. We propose the first image inpainting framework based on 2D Gaussian Splatting, which encodes incomplete images into a continuous field of 2D Gaussian splat coefficients and reconstructs the final image via a differentiable rasterization process. The continuous rendering paradigm of GS inherently promotes pixel-level coherence in the inpainted results. To improve efficiency and scalability, we introduce a patch-wise rasterization strategy that reduces memory overhead and accelerates inference. For global semantic consistency, we incorporate features from a pretrained DINO model. We observe that DINO's global features are naturally robust to small missing regions and can be effectively adapted to guide semantic alignment in large-mask scenarios, ensuring that the inpainted content remains contextually consistent with the surrounding scene. Extensive experiments on standard benchmarks demonstrate that our method achieves competitive performance in both quantitative metrics and perceptual quality, establishing a new direction for applying Gaussian Splatting to 2D image processing.
Abstract (translated)
高斯点绘（GS）是一种最近用于将离散点转换为连续空间表示的技术，在3D场景建模和2D图像超分辨率方面已显示出令人鼓舞的结果。在这篇论文中，我们探索了它在图像修复中的未开发潜力，该领域需要局部连贯的像素合成以及全局一致的语义恢复。我们提出了首个基于二维高斯点绘的图像修补框架，将不完整的图像编码为2D高斯点绘系数的连续场，并通过可微渲染过程重构最终图像。GS的连续渲染范式内在地促进了修复结果中的像素级连贯性。 为了提高效率和扩展性，我们引入了一种基于块的渲染策略，减少了内存占用并加速了推断速度。对于全局语义一致性，我们结合了一个预训练的DINO模型的功能。我们观察到，DINO的全局特征对小缺失区域具有自然鲁棒性，并且可以有效地适应以指导大掩码场景中的语义对齐，确保修复内容与周围场景保持上下文一致。 在标准基准上的广泛实验表明，我们的方法在定量指标和感知质量方面均实现了竞争性的性能，为应用高斯点绘技术于二维图像处理开辟了新的方向。
URL
https://arxiv.org/abs/2509.01964
