SVLGaussian: Single View Language Gaussian Splatting

Jiachen Wang , Mingyang Ding , Min Tan , Luocheng Zhang , Jingrui Fan , Wenwen Pan , Zhou Yu , Jiajun Ding

CAAI Transactions on Intelligence Technology ›› 2026, Vol. 11 ›› Issue (3) : 875 -899.

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CAAI Transactions on Intelligence Technology ›› 2026, Vol. 11 ›› Issue (3) :875 -899. DOI: 10.1049/cit2.70148
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SVLGaussian: Single View Language Gaussian Splatting
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Abstract

Open-vocabulary 3D querying based on 3D Gaussian splatting (3DGS) shows great promise in facilitating accurate 3D query capabilities of AI systems. These methods typically rely on pre-captured multi-view images to enable natural language interactions with 3D scenes. In practice, when embodied AI encounters unexplored scenes, it is difficult to obtain observations from different viewpoints beforehand. This challenge highlights the importance of exploring natural language-driven 3D scene querying from a single current viewpoint. This paper proposes single view language Gaussian splatting (SVLGaussian) for the novel task: Open-vocabulary 3D querying based on the input single view. By leveraging multi-round inference of multimodal large language models, SVLGaussian efficiently generates pixel-level semantic probabilities and rapidly embeds them into a 3D Gaussian field, enabling real-time language-guided semantic querying. To verify our model, we annotated three datasets: Lerf_ovs and 3D-OVS, which are tailored for open-vocabulary 3D querying, and RE10K, which is adapted for single-view 3D reconstruction. Both quantitative and qualitative results show that our method effectively supports open-vocabulary 3D querying from a single view.

Keywords

Gaussian splatting / language-driven semantic querying / single-view 3D reconstruction

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Jiachen Wang, Mingyang Ding, Min Tan, Luocheng Zhang, Jingrui Fan, Wenwen Pan, Zhou Yu, Jiajun Ding. SVLGaussian: Single View Language Gaussian Splatting. CAAI Transactions on Intelligence Technology, 2026, 11 (3) : 875-899 DOI:10.1049/cit2.70148

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Acknowledgements

This work was supported by the Key Research and Development Program of Zhejiang Province (Grant No. 2025C01026); the National College Students Innovation and Entrepreneurship Training Program of the Ministry of Education of the People's Republic of China (Grant No. 202410336024); the National Natural Science Foundation of China (Grant Nos. 62206082, 62402152, 62472133 and 62422204) and the Natural Science Foundation of Zhejiang Province (Grant Nos. LDT23F02025F02, LQN25F020017 and LRG26F020001).

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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