SLGaussian: Fast Language Gaussian Splatting in Sparse Views

TL;DR

SLGaussian is a fast, feed-forward method that constructs 3D semantic fields from sparse viewpoints, enabling efficient and accurate 3D scene understanding with language integration, outperforming existing approaches.

Contribution

It introduces SLGaussian, a novel approach that embeds language into 3D space efficiently from sparse views, avoiding costly per-scene optimization.

Findings

Outperforms existing methods in IoU, localization, and mIoU.

Scene inference time is under 30 seconds.

Open-vocabulary querying takes only 0.011 seconds per query.

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 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.