Voronoi-guided Bilateral 2D Gaussian Splatting for Arbitrary-Scale Hyperspectral Image Super-Resolution

• URL: http://arxiv.org/abs/2604.17727v1
• Date: Mon, 20 Apr 2026 02:21:52 GMT
• Title: Voronoi-guided Bilateral 2D Gaussian Splatting for Arbitrary-Scale Hyperspectral Image Super-Resolution
• Abstract summary: Voronoi-Guided Bilateral 2D Gaussian Splatting for spatial reconstruction.<n>Experiments on benchmark datasets demonstrate the effectiveness of GaussianHSI over state-of-the-art methods for arbitrary-scale hyperspectral image super-resolution.
• Score: 42.38129717287735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing hyperspectral image super-resolution methods require modifications for different scales, limiting their flexibility in arbitrary-scale reconstruction. 2D Gaussian splatting provides a continuous representation that is compatible with arbitrary-scale super-resolution. Existing methods often rely on rasterization strategies, which may limit flexible spatial modeling. Extending them to hyperspectral image super-resolution remains challenging, as the task requires adaptive spatial reconstruction while preserving spectral fidelity. This paper proposes GaussianHSI, a Gaussian-Splatting-based framework for arbitrary-scale hyperspectral image super-resolution. We develop a Voronoi-Guided Bilateral 2D Gaussian Splatting for spatial reconstruction. After predicting a set of Gaussian functions to represent the input, it associates each target pixel with relevant Gaussian functions through Voronoi-guided selection. The target pixel is then reconstructed by aggregating the selected Gaussian functions with reference-aware bilateral weighting, which considers both geometric relevance and consistency with low-resolution features. We further introduce a Spectral Detail Enhancement module to improve spectral reconstruction. Extensive experiments on benchmark datasets demonstrate the effectiveness of GaussianHSI over state-of-the-art methods for arbitrary-scale hyperspectral image super-resolution.

Related papers

• CLEAR: Conflict-aware Learning via Evidence-guided Adaptive Routing for Unified Sparse-View 3D Gaussian Super-Resolution [80.43237927269575]
  We propose CLEAR as the first unified single-stage framework for Sparse-view 3D Gaussian Splatting Super-resolution.<n> CLEAR performs joint the optimization of authentic LR observations and external HR priors within a unified Gaussian representation.<n>Experiments on both synthetic and real-world $4times$ super-resolution benchmarks demonstrate that CLEAR consistently achieves state-of-the-art rendering quality.
  arXiv  Detail & Related papers  (2026-08-03T13:31:53Z)
• GauSSmart: Enhanced 3D Reconstruction through 2D Foundation Models and Geometric Filtering [50.675710727721786]
  We propose GauSSmart, a hybrid method that bridges 2D foundational models and 3D Gaussian Splatting reconstruction.<n>Our approach integrates established 2D computer vision techniques, including convex filtering and semantic feature supervision.<n>We validate our approach across three datasets, where GauSSmart consistently outperforms existing Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-10-16T03:38:26Z)
• Compact Multi-level-prior Tensor Representation for Hyperspectral Image Super-resolution [12.432457307235092]
  Fusing a hyperspectral image with a multispectral image over the same scene, textiti.e., hyperspectral image super-resolution, has become a popular computational way to access the high-spatial-spectral-resolution image.<n>We present a novel hyperspectral superresolution model compactly characterizing these multilevel priors of hyperspectral images within the tensor framework.
  arXiv  Detail & Related papers  (2025-10-07T16:26:34Z)
• Anti-Aliased 2D Gaussian Splatting [16.042394978941516]
  2D Gaussian Splatting (2DGS) has emerged as a promising method for novel view synthesis and surface reconstruction.<n>2DGS suffers from severe aliasing artifacts when rendering at different sampling rates than those used during training.<n>We present AA-2DGS, an anti-aliased formulation of 2D Gaussian Splatting that maintains its geometric benefits while significantly enhancing rendering quality across different scales.
  arXiv  Detail & Related papers  (2025-06-12T19:49:57Z)
• A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution [4.487807378174191]
  We propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN)<n>Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information.<n>An Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies.<n>To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear with depthwise separable convolutions.
  arXiv  Detail & Related papers  (2025-05-06T11:15:59Z)
• EigenGS Representation: From Eigenspace to Gaussian Image Space [20.454762899389358]
  EigenGS is an efficient transformation pipeline connecting eigenspace and image-space Gaussian representations.<n>We show that EigenGS achieves superior reconstruction quality compared to direct 2D Gaussian fitting.<n>The results highlight EigenGS's effectiveness and generalization ability across images with varying resolutions and diverse categories.
  arXiv  Detail & Related papers  (2025-03-10T15:27:03Z)
• Pixel to Gaussian: Ultra-Fast Continuous Super-Resolution with 2D Gaussian Modeling [50.34513854725803]
  Arbitrary-scale super-resolution (ASSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs with arbitrary upsampling factors.<n>We propose a novel ContinuousSR framework with a Pixel-to-Gaussian paradigm, which explicitly reconstructs 2D continuous HR signals from LR images using Gaussian Splatting.
  arXiv  Detail & Related papers  (2025-03-09T13:43:57Z)
• S2Gaussian: Sparse-View Super-Resolution 3D Gaussian Splatting [47.75073170368562]
  We propose a novel Sparse-view Super-resolution 3D Gaussian Splatting framework, dubbed S2Gaussian, that can reconstruct structure-accurate and detail-faithful 3D scenes with only sparse and low-resolution views.<n>Experiments demonstrate superior results and in particular establishing new state-of-the-art performances with more consistent geometry and finer details.
  arXiv  Detail & Related papers  (2025-03-06T10:58:26Z)
• GPS-Gaussian: Generalizable Pixel-wise 3D Gaussian Splatting for Real-time Human Novel View Synthesis [70.24111297192057]
  We present a new approach, termed GPS-Gaussian, for synthesizing novel views of a character in a real-time manner. The proposed method enables 2K-resolution rendering under a sparse-view camera setting.
  arXiv  Detail & Related papers  (2023-12-04T18:59:55Z)

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.