EditSplat：基于多视图融合与注意力引导优化的3D高斯溅射视图一致3D场景编辑
EditSplat: Multi-View Fusion and Attention-Guided Optimization for View-Consistent 3D Scene Editing with 3D Gaussian Splatting
AI总结：
针对当前3D编辑方法多视图不一致、3D高斯溅射优化低效的问题，提出EditSplat框架，通过多视图融合引导确保一致性，注意力引导优化提升效率，为文本驱动3D场景编辑建立新基准。
摘要：
近期3D编辑领域的进展凸显了文本驱动方法在实时、用户友好的AR/VR应用中的潜力。然而，当前方法依赖2D扩散模型却未充分考虑多视图信息，导致多视图不一致。尽管3D高斯溅射（3DGS）显著提升了渲染质量和速度，但其3D编辑过程存在优化效率低下的问题，因为预训练高斯保留过多源信息，阻碍优化。为解决这些局限，我们提出EditSplat，一种新颖的文本驱动3D场景编辑框架，集成多视图融合引导（MFG）和注意力引导优化（AGT）。MFG通过将关键多视图信息融入扩散过程，利用文本到图像扩散模型的无分类器引导和3DGS固有的几何结构，确保多视图一致性。此外，AGT利用3DGS的显式表示选择性修剪和优化3D高斯，提升优化效率并实现精确、语义丰富的局部编辑。通过广泛的定性和定量评估，EditSplat达到了最先进的性能，为文本驱动3D场景编辑建立了新基准。
英文摘要：
Recent advancements in 3D editing have highlighted the potential of text-driven methods in real-time, user-friendly AR/VR applications. However, current methods rely on 2D diffusion models without adequately considering multi-view information, resulting in multi-view inconsistency. While 3D Gaussian Splatting (3DGS) significantly improves rendering quality and speed, its 3D editing process encounters difficulties with inefficient optimization, as pre-trained Gaussians retain excessive source information, hindering optimization. To address these limitations, we propose EditSplat, a novel text-driven 3D scene editing framework that integrates Multi-view Fusion Guidance (MFG) and Attention-Guided Trimming (AGT). Our MFG ensures multi-view consistency by incorporating essential multi-view information into the diffusion process, leveraging classifier-free guidance from the text-to-image diffusion model and the geometric structure inherent to 3DGS. Additionally, our AGT utilizes the explicit representation of 3DGS to selectively prune and optimize 3D Gaussians, enhancing optimization efficiency and enabling precise, semantically rich local editing. Through extensive qualitative and quantitative evaluations, EditSplat achieves state-of-the-art performance, establishing a new benchmark for text-driven 3D scene editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
