A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912543400787968 |
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| author | Zaouali, Mahmoud Chick Charter, Todd Karpichev, Yehor Haworth, Brandon Najjaran, Homayoun |
| author_facet | Zaouali, Mahmoud Chick Charter, Todd Karpichev, Yehor Haworth, Brandon Najjaran, Homayoun |
| contents | Gaussian Splatting has rapidly emerged as a transformative technique for real-time 3D scene representation, offering a highly efficient and expressive alternative to Neural Radiance Fields (NeRF). Its ability to render complex scenes with high fidelity has enabled progress across domains such as scene reconstruction, robotics, and interactive content creation. More recently, the integration of Large Language Models (LLMs) and language embeddings into Gaussian Splatting pipelines has opened new possibilities for text-conditioned generation, editing, and semantic scene understanding. Despite these advances, a comprehensive overview of this emerging intersection has been lacking. This survey presents a structured review of current research efforts that combine language guidance with 3D Gaussian Splatting, detailing theoretical foundations, integration strategies, and real-world use cases. We highlight key limitations such as computational bottlenecks, generalizability, and the scarcity of semantically annotated 3D Gaussian data and outline open challenges and future directions for advancing language-guided 3D scene understanding using Gaussian Splatting. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_05064 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding Zaouali, Mahmoud Chick Charter, Todd Karpichev, Yehor Haworth, Brandon Najjaran, Homayoun Graphics Computation and Language Computer Vision and Pattern Recognition Gaussian Splatting has rapidly emerged as a transformative technique for real-time 3D scene representation, offering a highly efficient and expressive alternative to Neural Radiance Fields (NeRF). Its ability to render complex scenes with high fidelity has enabled progress across domains such as scene reconstruction, robotics, and interactive content creation. More recently, the integration of Large Language Models (LLMs) and language embeddings into Gaussian Splatting pipelines has opened new possibilities for text-conditioned generation, editing, and semantic scene understanding. Despite these advances, a comprehensive overview of this emerging intersection has been lacking. This survey presents a structured review of current research efforts that combine language guidance with 3D Gaussian Splatting, detailing theoretical foundations, integration strategies, and real-world use cases. We highlight key limitations such as computational bottlenecks, generalizability, and the scarcity of semantically annotated 3D Gaussian data and outline open challenges and future directions for advancing language-guided 3D scene understanding using Gaussian Splatting. |
| title | A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding |
| topic | Graphics Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.05064 |