SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition

• URL: http://arxiv.org/abs/2401.17857v4
• Date: Sun, 19 Jan 2025 08:31:42 GMT
• Title: SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition
• Authors: Xu Hu, Yuxi Wang, Lue Fan, Chuanchen Luo, Junsong Fan, Zhen Lei, Qing Li, Junran Peng, Zhaoxiang Zhang,
• Abstract summary: 3D Gaussian Splatting has emerged as an alternative 3D representation for novel view synthesis. We propose SAGD, a conceptually simple yet effective boundary-enhanced segmentation pipeline for 3D-GS. Our approach achieves high-quality 3D segmentation without rough boundary issues, which can be easily applied to other scene editing tasks.
• Score: 66.56357905500512
• License:
• Abstract: 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.

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