SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition
This addresses a specific issue in 3D scene editing for computer vision applications, representing an incremental improvement over existing methods.
The paper tackled the problem of rough boundaries in 3D Gaussian Splatting for object segmentation by proposing SAGD, a pipeline that uses Gaussian Decomposition to enhance boundaries, resulting in high-quality 3D segmentation without sacrificing speed.
3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis, benefiting from its high-quality rendering results and real-time rendering speed. However, the 3D Gaussians learned by 3D-GS have ambiguous structures without any geometry constraints. This inherent issue in 3D-GS leads to a rough boundary when segmenting individual objects. To remedy these problems, we propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS to improve segmentation accuracy while preserving segmentation speed. Specifically, we introduce a Gaussian Decomposition scheme, which ingeniously utilizes the special structure of 3D Gaussian, finds out, and then decomposes the boundary Gaussians. Moreover, to achieve fast interactive 3D segmentation, we introduce a novel training-free pipeline by lifting a 2D foundation model to 3D-GS. Extensive experiments demonstrate that our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.