Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression

• URL: http://arxiv.org/abs/2512.24018v1
• Date: Tue, 30 Dec 2025 06:35:46 GMT
• Title: Structure-Guided Allocation of 2D Gaussians for Image Representation and Compression
• Authors: Huanxiong Liang, Yunuo Chen, Yicheng Pan, Sixian Wang, Jincheng Dai, Guo Lu, Wenjun Zhang,
• Abstract summary: We propose a structure-guided allocation principle for 2DGS, which explicitly couples image structure with both representation capacity and quantization precision.<n>We show that our approach substantially improves both the representational power and the gradient performance of 2DGS while maintaining over 1000 FPS decoding.
• Score: 26.855464287699366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in 2D Gaussian Splatting (2DGS) have demonstrated its potential as a compact image representation with millisecond-level decoding. However, existing 2DGS-based pipelines allocate representation capacity and parameter precision largely oblivious to image structure, limiting their rate-distortion (RD) efficiency at low bitrates. To address this, we propose a structure-guided allocation principle for 2DGS, which explicitly couples image structure with both representation capacity and quantization precision, while preserving native decoding speed. First, we introduce a structure-guided initialization that assigns 2D Gaussians according to spatial structural priors inherent in natural images, yielding a localized and semantically meaningful distribution. Second, during quantization-aware fine-tuning, we propose adaptive bitwidth quantization of covariance parameters, which grants higher precision to small-scale Gaussians in complex regions and lower precision elsewhere, enabling RD-aware optimization, thereby reducing redundancy without degrading edge quality. Third, we impose a geometry-consistent regularization that aligns Gaussian orientations with local gradient directions to better preserve structural details. Extensive experiments demonstrate that our approach substantially improves both the representational power and the RD performance of 2DGS while maintaining over 1000 FPS decoding. Compared with the baseline GSImage, we reduce BD-rate by 43.44% on Kodak and 29.91% on DIV2K.

Related papers

• Fast 2DGS: Efficient Image Representation with Deep Gaussian Prior [21.89104780995278]
  Fast-2DGS is a lightweight framework for efficient Gaussian image representation.<n>We introduce Deep Gaussian Prior, implemented as a conditional network to capture the spatial distribution of Gaussian primitives.<n> Experiments demonstrate that this disentangled architecture achieves high-quality reconstruction in a single forward pass, followed by minimal fine-tuning.
  arXiv  Detail & Related papers  (2025-12-14T17:23:28Z)
• 2D Gaussian Splatting with Semantic Alignment for Image Inpainting [46.266955851252504]
  We propose the first image inpainting framework based on 2D Gaussian Splatting.<n>For global semantic consistency, we incorporate features from a pretrained DINO model.<n>Our method achieves competitive performance in both quantitative metrics and perceptual quality.
  arXiv  Detail & Related papers  (2025-09-02T05:12:52Z)
• ADC-GS: Anchor-Driven Deformable and Compressed Gaussian Splatting for Dynamic Scene Reconstruction [22.24139713363786]
  Existing 4D Gaussian Splatting methods rely on per-Gaussian deformation from a canonical space to target frames.<n>We propose Anchor-Driven Deformable and Compressed Gaussian Splatting (ADC-GS), a compact and efficient representation for dynamic scene reconstruction.<n>We show that ADC-GS outperforms the per-Gaussian deformation approaches in rendering speed by 300%-800%.
  arXiv  Detail & Related papers  (2025-05-13T03:13:40Z)
• Steepest Descent Density Control for Compact 3D Gaussian Splatting [72.54055499344052]
  3D Gaussian Splatting (3DGS) has emerged as a powerful real-time, high-resolution novel view.<n>We propose a theoretical framework that demystifies and improves density control in 3DGS.<n>We introduce SteepGS, incorporating steepest density control, a principled strategy that minimizes loss while maintaining a compact point cloud.
  arXiv  Detail & Related papers  (2025-05-08T18:41:38Z)
• Parallel Sequence Modeling via Generalized Spatial Propagation Network [80.66202109995726]
  Generalized Spatial Propagation Network (GSPN) is a new attention mechanism for optimized vision tasks that inherently captures 2D spatial structures.<n>GSPN overcomes limitations by directly operating on spatially coherent image data and forming dense pairwise connections through a line-scan approach.<n>GSPN achieves superior spatial fidelity and state-of-the-art performance in vision tasks, including ImageNet classification, class-guided image generation, and text-to-image generation.
  arXiv  Detail & Related papers  (2025-01-21T18:56:19Z)
• CityGaussianV2: Efficient and Geometrically Accurate Reconstruction for Large-Scale Scenes [53.107474952492396]
  CityGaussianV2 is a novel approach for large-scale scene reconstruction.<n>We implement a decomposed-gradient-based densification and depth regression technique to eliminate blurry artifacts and accelerate convergence.<n>Our method strikes a promising balance between visual quality, geometric accuracy, as well as storage and training costs.
  arXiv  Detail & Related papers  (2024-11-01T17:59:31Z)
• InstantSplat: Sparse-view Gaussian Splatting in Seconds [91.77050739918037]
  We introduce InstantSplat, a novel approach for addressing sparse-view 3D scene reconstruction at lightning-fast speed.<n>InstantSplat employs a self-supervised framework that optimize 3D scene representation and camera poses.<n>It achieves an acceleration of over 30x in reconstruction and improves visual quality (SSIM) from 0.3755 to 0.7624 compared to traditional SfM with 3D-GS.
  arXiv  Detail & Related papers  (2024-03-29T17:29:58Z)
• CoherentGS: Sparse Novel View Synthesis with Coherent 3D Gaussians [18.42203035154126]
  We introduce a structured Gaussian representation that can be controlled in 2D image space.<n>We then constraint the Gaussians, in particular their position, and prevent them from moving independently during optimization.<n>We demonstrate significant improvements compared to the state-of-the-art sparse-view NeRF-based approaches on a variety of scenes.
  arXiv  Detail & Related papers  (2024-03-28T15:27:13Z)
• LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS [55.85673901231235]
  We introduce LightGaussian, a method for transforming 3D Gaussians into a more compact format. Inspired by Network Pruning, LightGaussian identifies Gaussians with minimal global significance on scene reconstruction. LightGaussian achieves an average 15x compression rate while boosting FPS from 144 to 237 within the 3D-GS framework.
  arXiv  Detail & Related papers  (2023-11-28T21:39:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.