AI RESEARCH
SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation
arXiv CS.CV
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ArXi:2603.07789v1 Announce Type: new 2D Gaussian Splatting has emerged as a novel image representation technique that can efficient rendering on low-end devices. However, scaling to high-resolution images requires optimizing and storing millions of unstructured Gaussian primitives independently, leading to slow convergence and redundant parameters. To address this, we propose Structured Gaussian Image (SGI), a compact and efficient framework for representing high-resolution images. SGI decomposes a complex image into multi-scale local spaces defined by a set of seeds.