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 SLGaussian, 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, SLGaussian 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, SLGaussian 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.
@article{arxiv.2412.08331,
title = {SLGaussian: Fast Language Gaussian Splatting in Sparse Views},
author = {Kangjie Chen and BingQuan Dai and Minghan Qin and Dongbin Zhang and Peihao Li and Yingshuang Zou and Haoqian Wang},
journal= {arXiv preprint arXiv:2412.08331},
year = {2025}
}
Comments
Accepted by ACM MM 2025. Project page: https://chenkangjie1123.github.io/SLGaussian.github.io/