SLAG: Scalable Language-Augmented Gaussian Splatting

• URL: http://arxiv.org/abs/2505.08124v1
• Date: Mon, 12 May 2025 23:32:24 GMT
• Title: SLAG: Scalable Language-Augmented Gaussian Splatting
• Authors: Laszlo Szilagyi, Francis Engelmann, Jeannette Bohg,
• Abstract summary: Language-augmented scene representations hold great promise for large-scale robotics applications such as search-and-rescue, smart cities, and mining.<n>Many of these scenarios are time-sensitive, requiring rapid scene encoding while also being data-intensive, necessitating scalable solutions.<n>We introduce SLAG, a multi-GPU framework for language-augmented Gaussian splatting that enhances the speed and scalability of embedding large scenes.
• Score: 19.643023058839603
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 and CLIP. 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 times speedup in embedding computation on a 16-GPU setup compared to OpenGaussian, while preserving embedding quality on the ScanNet and LERF datasets. For more details, visit our project website: https://slag-project.github.io/.

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