SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation

• URL: http://arxiv.org/abs/2603.07789v2
• Date: Tue, 10 Mar 2026 16:21:10 GMT
• Title: SGI: Structured 2D Gaussians for Efficient and Compact Large Image Representation
• Abstract summary: 2D Gaussian Splatting has emerged as a novel image representation technique that can support efficient rendering on low-end devices.<n>We propose Structured Gaussian Image (SGI), a compact and efficient framework for representing high-resolution images.<n>SGI decomposes a complex image into multi-scale local spaces defined by a set of seeds.
• Score: 15.343904266924445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 2D Gaussian Splatting has emerged as a novel image representation technique that can support 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. Each seed corresponds to a spatially coherent region and, together with lightweight multi-layer perceptrons (MLPs), generates structured implicit 2D neural Gaussians. This seed-based formulation imposes structural regularity on otherwise unstructured Gaussian primitives, which facilitates entropy-based compression at the seed level to reduce the total storage. However, optimizing seed parameters directly on high-resolution images is a challenging and non-trivial task. Therefore, we designed a multi-scale fitting strategy that refines the seed representation in a coarse-to-fine manner, substantially accelerating convergence. Quantitative and qualitative evaluations demonstrate that SGI achieves up to 7.5x compression over prior non-quantized 2D Gaussian methods and 1.6x over quantized ones, while also delivering 1.6x and 6.5x faster optimization, respectively, without degrading, and often improving, image fidelity. Code is available at https://github.com/zx-pan/SGI.

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