  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.
