TIGER：文本指导的3D高斯检索与连贯编辑
TIGER: Text-Instructed 3D Gaussian Retrieval and Coherent Editing
AI总结：
本文提出TIGER方法，通过自下而上的语言聚合策略生成密集语言嵌入3D高斯以支持开放词汇检索，并利用连贯分数蒸馏聚合2D图像编辑和多视图扩散模型，实现多视图一致的文本指导3D高斯检索与精细编辑。
摘要：
编辑场景中的对象是计算机视觉和图形学众多应用所需的关键功能。随着3D Gaussian Splatting（3DGS）成为场景表示的前沿技术，对3D高斯场景的有效修改变得日益重要。这一过程需要准确检索目标对象，并随后根据指令进行修改。尽管现有技术部分可用，但它们主要将稀疏语义嵌入高斯以进行检索，并依赖迭代数据集更新范式进行编辑，导致过度平滑或不一致问题。为此，本文提出一种系统方法，即TIGER，用于连贯的文本指导3D高斯检索与编辑。与3D高斯的自上而下语言基础方法不同，我们采用自下而上的语言聚合策略，生成支持开放词汇检索的更密集语言嵌入3D高斯。为克服编辑中的过度平滑和不一致问题，我们提出连贯分数蒸馏（CSD），聚合2D图像编辑扩散模型和多视图扩散模型进行分数蒸馏，产生具有更精细细节的多视图一致编辑。在各种实验中，我们证明TIGER能够比先前工作实现更一致和真实的编辑。
英文摘要：
Editing objects within a scene is a critical functionality required across a broad spectrum of applications in computer vision and graphics. As 3D Gaussian Splatting (3DGS) emerges as a frontier in scene representation, the effective modification of 3D Gaussian scenes has become increasingly vital. This process entails accurately retrieve the target objects and subsequently performing modifications based on instructions. Though available in pieces, existing techniques mainly embed sparse semantics into Gaussians for retrieval, and rely on an iterative dataset update paradigm for editing, leading to over-smoothing or inconsistency issues. To this end, this paper proposes a systematic approach, namely TIGER, for coherent text-instructed 3D Gaussian retrieval and editing. In contrast to the top-down language grounding approach for 3D Gaussians, we adopt a bottom-up language aggregation strategy to generate a denser language embedded 3D Gaussians that supports open-vocabulary retrieval. To overcome the over-smoothing and inconsistency issues in editing, we propose a Coherent Score Distillation (CSD) that aggregates a 2D image editing diffusion model and a multi-view diffusion model for score distillation, producing multi-view consistent editing with much finer details. In various experiments, we demonstrate that our TIGER is able to accomplish more consistent and realistic edits than prior work.
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。
