CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation

• URL: http://arxiv.org/abs/2609.08730v1
• Date: Tue, 08 Sep 2026 13:26:32 GMT
• Title: CVT-GS: Learning to Simplify 3D Gaussian Splatting with Centroidal Voronoi Tessellation
• Abstract summary: 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis.<n> rendering high-fidelity scenes often relies on a massive number of Gaussian primitives.<n>Existing simplification techniques are largely intrusive, requiring training-time pruning, architectural modifications, or computationally expensive per-scene fine-tuning.<n>We propose Centro-GS, a novel optimization-free post-hoc simplification framework that directly compresses trained 3DGS scenes without sacrificing visual fidelity.
• Score: 42.60357708039613
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While 3D Gaussian Splatting (3DGS) has emerged as a powerful representation for real-time novel view synthesis, rendering high-fidelity scenes often relies on a massive number of Gaussian primitives, incurring substantial storage and computational overhead. Existing simplification techniques are largely intrusive, requiring training-time pruning, architectural modifications, or computationally expensive per-scene fine-tuning. These drawbacks limit their deployment on off-the-shelf pretrained models. In this paper, we propose CVT-GS, a novel optimization-free post-hoc simplification framework that directly compresses trained 3DGS scenes without sacrificing visual fidelity. Our approach first constructs spatially coherent cells over Gaussian centers via a geometry-aware Centroidal Voronoi Tessellation (CVT). Subsequently, a lightweight neural cell merger predicts the geometry and appearance of a single, highly representative Gaussian primitive for each cell under differentiable rendering supervision. By formulating simplification as a rendering-aware many-to-one merging process rather than naive primitive pruning, CVT-GS outputs a standard 3DGS scene that is seamlessly compatible with existing renderers. Experiments on various datasets demonstrate the superiority of our method. Notably, when achieving a 100-fold reduction in Gaussian points, our method operates 12 times faster than state-of-the-art methods while improving the PSNR by 1.3 dB.

Related papers

• Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives [14.295266671241004]
  3D Gaussian Splatting (3DGS) has enabled real-time rendering with photorealistic quality.<n>3DGS often requires a large number of primitives to achieve high fidelity.<n>We propose an efficient, integrated reconstruction-aware pruning strategy that determines pruning timing and refining intervals.<n>We also introduce a 3D Difference-of-Gaussians primitive that jointly models both positive and negative densities in a single primitive.
  arXiv  Detail & Related papers  (2026-02-27T16:12:58Z)
• Segmentation-Driven Initialization for Sparse-view 3D Gaussian Splatting [0.9251324073335035]
  3D Gaussian Splatting (3DGS) has enabled real-time rendering with competitive quality.<n>Existing pipelines often rely on Structure-from-Motion (SfM) for camera pose estimation, an approach that struggles in genuinely sparse-view settings.<n>We propose a method that mitigates inefficiency by leveraging region-based segmentation to identify and retain only structurally significant regions.
  arXiv  Detail & Related papers  (2025-09-15T12:31:33Z)
• Gaussian Herding across Pens: An Optimal Transport Perspective on Global Gaussian Reduction for 3DGS [13.03299802566674]
  3DGS has emerged as a powerful technique for radiance field rendering, but it typically requires millions of redundant Gaussian primitives.<n>We propose a novel optimal transport perspective that casts 3DGS compaction as global Gaussian mixture reduction.<n>Our method is applicable to any stage of vanilla or accelerated 3DGS pipelines, providing an efficient and agnostic pathway to lightweight neural rendering.
  arXiv  Detail & Related papers  (2025-06-11T09:04:44Z)
• MonoGSDF: Exploring Monocular Geometric Cues for Gaussian Splatting-Guided Implicit Surface Reconstruction [86.87464903285208]
  We introduce MonoGSDF, a novel method that couples primitives with a neural Signed Distance Field (SDF) for high-quality reconstruction.<n>To handle arbitrary-scale scenes, we propose a scaling strategy for robust generalization.<n>Experiments on real-world datasets outperforms prior methods while maintaining efficiency.
  arXiv  Detail & Related papers  (2024-11-25T20:07:07Z)
• Geometric Algebra Planes: Convex Implicit Neural Volumes [70.12234371845445]
  We show that GA-Planes is equivalent to a sparse low-rank factor plus low-resolution matrix. We also show that GA-Planes can be adapted for many existing representations.
  arXiv  Detail & Related papers  (2024-11-20T18:21:58Z)
• 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)
• PUP 3D-GS: Principled Uncertainty Pruning for 3D Gaussian Splatting [59.277480452459315]
  We propose a principled sensitivity pruning score that preserves visual fidelity and foreground details at significantly higher compression ratios.<n>We also propose a multi-round prune-refine pipeline that can be applied to any pretrained 3D-GS model without changing its training pipeline.
  arXiv  Detail & Related papers  (2024-06-14T17:53:55Z)
• EfficientGS: Streamlining Gaussian Splatting for Large-Scale High-Resolution Scene Representation [29.334665494061113]
  'EfficientGS' is an advanced approach that optimize 3DGS for high-resolution, large-scale scenes. We analyze the densification process in 3DGS and identify areas of Gaussian over-proliferation. We propose a selective strategy, limiting Gaussian increase to key redundant primitives, thereby enhancing the representational efficiency.
  arXiv  Detail & Related papers  (2024-04-19T10:32:30Z)
• GaussianPro: 3D Gaussian Splatting with Progressive Propagation [49.918797726059545]
  3DGS relies heavily on the point cloud produced by Structure-from-Motion (SfM) techniques. We propose a novel method that applies a progressive propagation strategy to guide the densification of the 3D Gaussians. Our method significantly surpasses 3DGS on the dataset, exhibiting an improvement of 1.15dB in terms of PSNR.
  arXiv  Detail & Related papers  (2024-02-22T16:00:20Z)

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.