FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting

• URL: http://arxiv.org/abs/2511.23292v1
• Date: Fri, 28 Nov 2025 15:47:29 GMT
• Title: FACT-GS: Frequency-Aligned Complexity-Aware Texture Reparameterization for 2D Gaussian Splatting
• Authors: Tianhao Xie, Linlian Jiang, Xinxin Zuo, Yang Wang, Tiberiu Popa,
• Abstract summary: Texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity.<n>We introduce FACT-GS, a Frequency-Aligned Complexity-aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency.
• Score: 10.495318519671963
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent advances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussian sampling grid, allocating equal sampling density regardless of local visual complexity. This leads to inefficient texture space utilization, where high-frequency regions are under-sampled and smooth regions waste capacity, causing blurred appearance and loss of fine structural detail. We introduce FACT-GS, a Frequency-Aligned Complexity-aware Texture Gaussian Splatting framework that allocates texture sampling density according to local visual frequency. Grounded in adaptive sampling theory, FACT-GS reformulates texture parameterization as a differentiable sampling-density allocation problem, replacing the uniform textures with a learnable frequency-aware allocation strategy implemented via a deformation field whose Jacobian modulates local sampling density. Built on 2D Gaussian Splatting, FACT-GS performs non-uniform sampling on fixed-resolution texture grids, preserving real-time performance while recovering sharper high-frequency details under the same parameter budget.

Related papers

• A$^2$TG: Adaptive Anisotropic Textured Gaussians for Efficient 3D Scene Representation [7.103085444694659]
  Existing approaches allocate a fixed square texture per primitive, leading to inefficient memory usage and limited adaptability to scene variability.<n>We introduce adaptive anisotropic textured Gaussians (A$2$TG), a novel representation that generalizes textured Gaussians by equipping each primitive with an anisotropic texture.<n>Our method employs a gradient-guided adaptive rule to jointly determine texture resolution and aspect ratio, enabling non-uniform, detail-aware allocation.
  arXiv  Detail & Related papers  (2026-01-14T07:26:55Z)
• Joint Semantic and Rendering Enhancements in 3D Gaussian Modeling with Anisotropic Local Encoding [86.55824709875598]
  We propose a joint enhancement framework for 3D semantic Gaussian modeling that synergizes both semantic and rendering branches.<n>Unlike conventional point cloud shape encoding, we introduce an anisotropic 3D Gaussian Chebyshev descriptor to capture fine-grained 3D shape details.<n>We employ a cross-scene knowledge transfer module to continuously update learned shape patterns, enabling faster convergence and robust representations.
  arXiv  Detail & Related papers  (2026-01-05T18:33:50Z)
• ASAP-Textured Gaussians: Enhancing Textured Gaussians with Adaptive Sampling and Anisotropic Parameterization [51.51724817131134]
  3D Gaussian Splatting has texture parameterizations to capture spatially varying attributes.<n>Textures are typically defined in canonical space, leading to inefficient sampling.<n>Our proposed ASAP Textured Gaussians significantly improve the quality efficiency tradeoff, achieving high-fidelity rendering with far fewer texture parameters.
  arXiv  Detail & Related papers  (2025-12-16T03:13:27Z)
• Depth-Guided Bundle Sampling for Efficient Generalizable Neural Radiance Field Reconstruction [22.057122296909142]
  High-resolution images remain computationally intensive due to the need for dense sampling of all rays.<n>We propose a novel depth-guided bundle sampling strategy to accelerate rendering.<n>Our method achieves up to a 1.27 dB PSNR improvement and a 47% increase in FPS on the DTU dataset.
  arXiv  Detail & Related papers  (2025-05-26T10:23:59Z)
• GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction [79.42244344704154]
  GausSurf employs geometry guidance from multi-view consistency in texture-rich areas and normal priors in texture-less areas of a scene.<n>Our method surpasses state-of-the-art methods in terms of reconstruction quality and computation time.
  arXiv  Detail & Related papers  (2024-11-29T03:54:54Z)
• MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields [100.90743697473232]
  Radiance fields represented by 3D Gaussians excel at synthesizing novel views, offering both high training efficiency and fast rendering.<n>Existing methods often incorporate depth priors from dense estimation networks but overlook the inherent multi-view consistency in input images.<n>We propose a view synthesis framework based on 3D Gaussian Splatting, enabling scene reconstruction from sparse views.
  arXiv  Detail & Related papers  (2024-10-15T08:39:05Z)
• Textured-GS: Gaussian Splatting with Spatially Defined Color and Opacity [7.861993966048637]
  We introduce Textured-GS, an innovative method for rendering Gaussian splatting using Spherical Harmonics (SH) This approach enables each Gaussian to exhibit a richer representation by accommodating varying colors and opacities across its surface. Our experiments show that Textured-GS consistently outperforms both the baseline Mini-Splatting and standard 3DGS in terms of visual fidelity.
  arXiv  Detail & Related papers  (2024-07-13T00:45:37Z)
• DeferredGS: Decoupled and Editable Gaussian Splatting with Deferred Shading [50.331929164207324]
  We introduce DeferredGS, a method for decoupling and editing the Gaussian splatting representation using deferred shading. Both qualitative and quantitative experiments demonstrate the superior performance of DeferredGS in novel view and editing tasks.
  arXiv  Detail & Related papers  (2024-04-15T01:58:54Z)
• CVT-xRF: Contrastive In-Voxel Transformer for 3D Consistent Radiance Fields from Sparse Inputs [65.80187860906115]
  We propose a novel approach to improve NeRF's performance with sparse inputs. We first adopt a voxel-based ray sampling strategy to ensure that the sampled rays intersect with a certain voxel in 3D space. We then randomly sample additional points within the voxel and apply a Transformer to infer the properties of other points on each ray, which are then incorporated into the volume rendering.
  arXiv  Detail & Related papers  (2024-03-25T15:56:17Z)
• High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization [8.845446246585215]
  We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction. Compared to recent neural and concurrently developed gaussian splatting RGBD SLAM baselines, our method achieves state-of-the-art results on the synthetic dataset Replica and competitive results on the real-world dataset TUM.
  arXiv  Detail & Related papers  (2024-03-19T08:19:53Z)
• Mesh-based Gaussian Splatting for Real-time Large-scale Deformation [58.18290393082119]
  It is challenging for users to directly deform or manipulate implicit representations with large deformations in the real-time fashion. We develop a novel GS-based method that enables interactive deformation. Our approach achieves high-quality reconstruction and effective deformation, while maintaining the promising rendering results at a high frame rate.
  arXiv  Detail & Related papers  (2024-02-07T12:36:54Z)

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.