SLAG: Scalable Language-Augmented Gaussian Splatting - Szilagyi, Laszlo; Engelmann, Francis; Bohg, Jeannette (IEEE robotics and automation letters, 2025)
Language-augmented scene representations hold great promise for large-scale robotics applications such as search-and-rescue, smart cities, and mining. Many of these scenarios are time-sensitive, requiring rapid scene encoding while also being data-intensive, necessitating scalable solutions. Deploying these representations on robots with limited computational resources further adds to the challenge. To address this, we introduce SLAG, a multi-GPU framework for language-augmented Gaussian splatting that enhances the speed and scalability of embedding large scenes. Our method integrates 2D visual-language model features into 3D scenes using SAM (Kirillov et al., 2023) and CLIP (Radford et al., 2021). Unlike prior approaches, SLAG eliminates the need for a loss function to compute per-Gaussian language embeddings. Instead, it derives embeddings from 3D Gaussian scene parameters via a normalized weighted average, enabling highly parallelized scene encoding. Additionally, we introduce a vector database for efficient embedding storage and retrieval. Our experiments show that SLAG achieves an 18× speedup in embedding computation on a 16-GPU setup compared to OpenGaussian (Wu et al., 2024), while preserving embedding quality on the ScanNet (Dai et al., 2017) and LERF (Kerr et al., 2023) datasets.