SparseLGS: Fast Language Gaussian Splatting from Sparse Multi-View Images

Kangjie Chen, BingQuan Dai, Minghan Qin, DongbinZhang, Peihao Li, Haoqian Wang

Keywords: 3D Gaussian Splatting; 3D open-vocabulary query; 3D segmentation; sparse view reconstruction;

TL;DR: LangSparse constructs high-quality language scenes from just two RGB images, outperforming existing methods in dual-view 3D semantic queries.

Abstract: 3D semantic field learning is crucial for applications like autonomous navigation, AR/VR, and robotics, where accurate comprehension of 3D scenes from limited viewpoints is essential. Existing methods struggle under sparse view conditions, relying on inefficient per-scene multi-view optimizations, which are impractical for many real-world tasks. To address this, we propose SparseLGS, a feed-forward method for constructing 3D semantic fields from sparse viewpoints, allowing direct inference of 3DGS-based scenes. By ensuring consistent SAM segmentations through video tracking and using low-dimensional indexing for high-dimensional CLIP features, SparseLGS efficiently embeds language information in 3D space, offering a robust solution for accurate 3D scene understanding under sparse view conditions. In experiments on two-view sparse 3D object querying and segmentation in the LERF and 3D-OVS datasets, SparseLGS outperforms existing methods in chosen IoU, Localization Accuracy, and mIoU. Moreover, our model achieves scene inference in under 30 seconds and open-vocabulary querying in just 0.011 seconds per query.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1765