Language Embedded 3D Gaussians for Open-Vocabulary Scene Understanding
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Abstract (arXiv)
Open-vocabulary querying in 3D space is challenging but essential for scene understanding tasks such as object localization and segmentation. Language-embedded scene representations have made progress by incorporating language features into 3D spaces. However, their efficacy heavily depends on neural networks that are resource-intensive in training and rendering. Although recent 3D Gaussians offer efficient and high-quality novel view synthesis, directly embedding language features in them leads to prohibitive memory usage and decreased performance. In this work, we introduce Language Embedded 3D Gaussians, a novel scene representation for open-vocabulary query tasks. Instead of embedding high-dimensional raw semantic features on 3D Gaussians, we propose a dedicated quantization scheme that drastically alleviates the memory requirement, and a novel embedding procedure that achieves smoother yet high accuracy query, countering the multi-view feature inconsistencies and the high-frequency inductive bias in point-based representations. Our comprehensive experiments show that our representation achieves the best visual quality and language querying accuracy across current language-embedded representations, while maintaining real-time rendering frame rates on a single desktop GPU.
Key facts. Cited by 208 papers in the map (data through 14 Oct 2026). Builds on 47 of its 64 references that are in the map. First posted on arXiv 30 Nov 2023.
Builds on
47 of 64 references.
References · 47
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- 2023AutoRecon: Automated 3D Object Discovery and Reconstructioncited by 13
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Citing papers per year: 2024: 49; 2025: 84; 2026: 75.
208 papers.
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- 2026DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians3 Sep 2026
- 2026VCAR: Training-Free 3DGS Segmentation via View Completeness and Axis-Aware Boundary Refinement31 Aug 2026
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