arXiv 2603.281262026-03-30cs.CV

SVGS: 通过高斯散射实现单视图到3D物体编辑

SVGS: Single-View to 3D Object Editing via Gaussian Splatting

School of Computer Science and Technology, Zhejiang Gongshang University（浙江工商大学计算机科学与技术学院）；Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology（浙江省大数据与未来电子商务技术重点实验室）；Jianpei Technology Co., Ltd.（建培科技有限公司）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University（沙特本·阿卜杜勒阿齐兹王子大学计算机工程与科学学院计算机科学系）；Department of Computer Science, Faculty of Computers and Information, Qena University（基纳大学计算机与信息学院计算机科学系）；Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与通信科学学院计算科学系）；Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院口腔颌面外科）；Systems Research Institute of the Polish Academy of Sciences（波兰科学院系统研究所）；AGH University of Krakow（AGH科技大学）；SAN University（SAN大学）

Pengcheng Xue, Yan Tian, Qiutao Song, Ziyi Wang, Linyang He, Weiping Ding, Mahmoud Hassaballah, Karen Egiazarian, Wei-Fa Yang, Leszek Rutkowski

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AI总结：

本文提出SVGS方法，通过单视图文本驱动编辑和稀疏3D高斯散射提升3D编辑效率与一致性，优于现有方法。

摘要：

文本驱动的3D场景编辑因其便利性和用户友好性而受到广泛关注。然而，依赖隐式3D表示的方法，如神经辐射场（NeRF），虽然在渲染复杂场景方面有效，但受到处理速度慢和对特定场景区域控制有限的限制。此外，现有方法，包括Instruct-NeRF2NeRF和GaussianEditor，利用多视图编辑策略时，执行文本指令时经常在不同视图之间产生不一致的结果。这种不一致性会影响模型的整体性能，使平衡编辑结果的一致性和编辑效率的任务变得更加复杂。为了解决这些挑战，我们提出了一种新的方法，称为通过高斯散射实现单视图到3D物体编辑（SVGS），这是一种基于3D高斯散射（3DGS）的单视图文本驱动编辑技术。具体来说，针对文本指令，我们引入了一种基于多视图扩散模型的单视图编辑策略，通过利用仅产生一致编辑结果的视图来重建3D场景。此外，我们采用稀疏3D高斯散射作为3D表示，这显著提高了编辑效率。我们对SVGS在各种场景设置下的现有基线方法进行了比较分析，结果表明，SVGS在编辑能力和处理速度上均优于其对手，代表了3D编辑技术的重大进步。更多详情请访问我们的项目页面：https://amateurc.github.io/svgs.github.io。

英文摘要：

Text-driven 3D scene editing has attracted considerable interest due to its convenience and user-friendliness. However, methods that rely on implicit 3D representations, such as Neural Radiance Fields (NeRF), while effective in rendering complex scenes, are hindered by slow processing speeds and limited control over specific regions of the scene. Moreover, existing approaches, including Instruct-NeRF2NeRF and GaussianEditor, which utilize multi-view editing strategies, frequently produce inconsistent results across different views when executing text instructions. This inconsistency can adversely affect the overall performance of the model, complicating the task of balancing the consistency of editing results with editing efficiency. To address these challenges, we propose a novel method termed Single-View to 3D Object Editing via Gaussian Splatting (SVGS), which is a single-view text-driven editing technique based on 3D Gaussian Splatting (3DGS). Specifically, in response to text instructions, we introduce a single-view editing strategy grounded in multi-view diffusion models, which reconstructs 3D scenes by leveraging only those views that yield consistent editing results. Additionally, we employ sparse 3D Gaussian Splatting as the 3D representation, which significantly enhances editing efficiency. We conducted a comparative analysis of SVGS against existing baseline methods across various scene settings, and the results indicate that SVGS outperforms its counterparts in both editing capability and processing speed, representing a significant advancement in 3D editing technology. For further details, please visit our project page at: https://amateurc.github.io/svgs.github.io.

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。