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SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition
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SAGD: Boundary-Enhanced Segment Anything in 3D Gaussian via Gaussian Decomposition
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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.
Forward citations
Cited by 12 Pith papers
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Online Segment 3D Gaussians via Launching Virtual Drones
SAGO achieves setup-free interactive 3D Gaussian segmentation by modeling it as an online NBV planning task in a Markov process, delivering sub-second latency and over 50x speedup over prior setup-free methods.
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BEA-GS: BEyond RAdiance Supervision in 3DGS for Precise Object Extraction
BEA-GS achieves superior object boundary segmentation in 3D Gaussian Splatting by introducing two new losses that adjust geometry of visible and non-visible Gaussians based on semantics.
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Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering
Fake3DGS benchmark shows state-of-the-art 2D fake detectors fail on 3D-manipulated Gaussian Splatting images while a new multi-view coherence method improves detection.
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STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
STaR-Quant provides a state-time consistent PTQ framework for DLLMs using SGAT and TAC to improve low-bit weight-activation quantization.
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Robust Prior-Guided Segmentation for Editable 3D Gaussian Splatting
A framework for robust 3D segmentation in editable Gaussian Splatting that combines SAM-HQ masks with prior-guided multiview-consistent label assignment to 3D Gaussians.
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NG-GS: NeRF-Guided 3D Gaussian Splatting Segmentation
NG-GS uses NeRF guidance and RBF interpolation on 3DGS to produce smoother, higher-quality object segmentation boundaries.
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Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation
A new framework generates part-level animatable 3D Gaussian vehicles from images by adding modules for exclusive part ownership and kinematic joint/axis prediction.
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Enhancing LLM Training via Spectral Clipping
SPECTRA improves LLM pretraining via post-clipping of update spectral norms and optional pre-clipping of gradient spikes, framed as Composite Frank-Wolfe regularization.
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LIVE-GS: LLM Powers Interactive VR Experience with Physics-Aware Gaussian Splatting
LIVE-GS uses an LLM to predict physical parameters from static Gaussian assets in 10 seconds for physics-aware VR interactions, validated by interviews, baseline comparisons, and user studies.
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Consistent Scene Understanding in 3D Gaussian Splatting via Multi-Cue Mask Refinement
A multi-cue mask refinement pipeline generates consistent 2D instance masks to guide 3D Gaussian Splatting optimization for stable scene segmentation.
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A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation
A survey that categorizes and summarizes methods applying 3D Gaussian Splatting to segmentation, editing, generation, and related tasks, including datasets and evaluation protocols.
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A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
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