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.

本文提出了名为 TIGER 的一种系统方法，用于一致的文本指导下的 3D 高斯检索和编辑，通过采用自底向上的语言聚合策略来生成支持开放词汇检索的更密集的语言嵌入的 3D 高斯场景，并通过聚合 2D 图像编辑扩散模型和多视图扩散模型的一致性得分蒸馏 (Coherent Score Distillation)，实现更一致、更真实的编辑效果。