← 研踪 PaperTrailCVF · CVPR 2026:cvf:a18902ac4fc76fac7bbd364076b9cd8d · 记录 #1
LangRef3DGS: Natural Language-Guided 3D Referential Segmentation from Partial Observations via 3D Gaussian Splatting
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 00:49
摘要
Language-guided 3D segmentation is crucial for linking 3D perception with semantic understanding, yet it remains vulnerable to the sparse and occluded views common in real-world RGB-D data. To overcome this, we present a real-time framework that leverages 3D Gaussian Splatting (3DGS) to build a semantically continuous and differentiable embedding field from partial observations. Our approach integrates two key components: a Dirichlet Process (DP) for the adaptive discovery of novel object categories, and a gradient low-rank mechanism that enhances class separability by reducing feature redundancy. This combination enables robust open-vocabulary segmentation guided directly by text prompts. Extensive experiments on challenging benchmarks demonstrate that our method achieves strong performance, exhibiting superior accuracy, robustness to partial inputs, and a powerful capacity for novel class discovery.Our code and models are available at https://github.com/Tap12345/LangGS.
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记录 #1 · 2026 · 最新观测版本
LangRef3DGS: Natural Language-Guided 3D Referential Segmentation from Partial Observations via 3D Gaussian Splatting
Language-guided 3D segmentation is crucial for linking 3D perception with semantic understanding, yet it remains vulnerable to the sparse and occluded views common in real-world RGB-D data. To overcome this, we present a real-time framework that leverages 3D Gaussian Splatting (3DGS) to build a semantically continuous and differentiable embedding field from partial observations. Our approach integrates two key components: a Dirichlet Process (DP) for the adaptive discovery of novel object categories, and a gradient low-rank mechanism that enhances class separability by reducing feature redundancy. This combination enables robust open-vocabulary segmentation guided directly by text prompts. Extensive experiments on challenging benchmarks demonstrate that our method achieves strong performance, exhibiting superior accuracy, robustness to partial inputs, and a powerful capacity for novel class discovery.Our code and models are available at https://github.com/Tap12345/LangGS.
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