Abstract
3D Gaussian inpainting, a critical technique for numerous applications in virtual reality and multimedia, has made significant progress with pretrained diffusion models. However, ensuring multi-view consistency, an essential requirement for high-quality inpainting, remains a key challenge. In this work, we present PAInpainter, a novel approach designed to advance 3D Gaussian inpainting by leveraging perspective-aware content propagation and consistency verification across multi-view inpainted images. Our method iteratively refines inpainting and optimizes the 3D Gaussian representation with multiple views adaptively sampled from a perspective graph. By propagating inpainted images as prior information and verifying consistency across neighboring views, PAInpainter substantially enhances global consistency and texture fidelity in restored 3D scenes. Extensive experiments demonstrate the superiority of PAInpainter over existing methods. Our approach achieves superior 3D inpainting quality, with PSNR scores of 26.03 dB and 29.51 dB on the SPIn-NeRF and NeRFiller datasets, respectively, highlighting its effectiveness and generalization capability.
Abstract (translated)
3D高斯插值是一种关键技术，广泛应用于虚拟现实和多媒体领域，并且通过预训练的扩散模型已经取得了显著进展。然而，确保多视角一致性仍然是实现高质量插值的一个重要挑战，这是高质量图像修复的关键要求之一。在本研究中，我们提出了PAInpainter，这是一种新颖的方法，旨在利用透视感知内容传播以及跨多个视图的插值图像的一致性验证来推进3D高斯插值技术。 我们的方法通过迭代优化从视角图自适应采样的多视角中的插值结果，并改进3D高斯表示。通过将已修复的图片作为先验信息进行传播，并检查相邻视角之间的连续性，PAInpainter能够显著提高恢复出的三维场景的整体一致性和纹理保真度。 广泛的实验表明了PAInpainter相较于现有方法的优势。我们的方法在SPIn-NeRF和NeRFiller数据集上分别取得了26.03 dB和29.51 dB的PSNR评分，这不仅展示了其卓越的质量，还证明了它具有良好的泛化能力。
URL
https://arxiv.org/abs/2510.10993
