arXiv:2605.30396v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) is a promising neural scene representation for real-time rendering, but trained models often suffer from large memory footprints, limiting deployment on less powerful devices. Existing compression techniques often lead to architectures with several additional trainable parameters. While achieving outstanding compression ratios, they introduce noticeable drops in image quality. In this work, we introduce the first dictionary-learning-based compression framework for 3DGS. The proposed post-training compression pipelin
The proliferation of 3D Gaussian Splatting (3DGS) models creates an immediate need for practical deployment solutions, accelerating research into compression techniques.
This development addresses a critical bottleneck in deploying advanced 3D AI models on resource-constrained devices, expanding their potential applications and market reach.
3DGS models can now be significantly smaller and faster to render, enabling broader adoption in areas like mobile AR/VR and embedded systems without substantial quality degradation.
- · Mobile AR/VR developers
- · Edge AI hardware manufacturers
- · 3D content creators
- · Gaming industry
- · Companies relying solely on high-end compute for 3D model deployment
More widespread deployment of realistic 3D content on everyday devices becomes feasible.
Increased demand for 3D capture and processing pipelines as the barrier to 3D consumption lowers.
The development of highly complex, interactive 3D environments that can run seamlessly on portable devices, fundamentally altering digital interaction paradigms.
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Read at arXiv cs.LG