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Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians

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arxiv 2403.14166 v3 pith:OTPPZH2S submitted 2024-03-21 cs.CV

Guangchi Fang , Bing Wang This is my paper · ORCID
classification cs.CV
keywords gaussiansmini-splattingconstrainednumberrepresentingscenesspatialacross
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In this study, we explore the challenge of efficiently representing scenes with a constrained number of Gaussians. Our analysis shifts from traditional graphics and 2D computer vision to the perspective of point clouds, highlighting the inefficient spatial distribution of Gaussian representation as a key limitation in model performance. To address this, we introduce strategies for densification including blur split and depth reinitialization, and simplification through intersection preserving and sampling. These techniques reorganize the spatial positions of the Gaussians, resulting in significant improvements across various datasets and benchmarks in terms of rendering quality, resource consumption, and storage compression. Our Mini-Splatting integrates seamlessly with the original rasterization pipeline, providing a strong baseline for future research in Gaussian-Splatting-based works. \href{https://github.com/fatPeter/mini-splatting}{Code is available}.

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Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. STREAMINGGS: Voxel-Based Streaming 3D Gaussian Splatting with Memory Optimization and Architectural Support

    cs.GR 2025-06 conditional novelty 7.0 of 10

    A voxel-based streaming pipeline and co-designed accelerator for 3D Gaussian Splatting is reported to cut DRAM traffic and achieve 45.7x average speedup and 62.9x energy savings over a mobile GPU in simulation.

  2. 3D Gaussian Splatting for Scientific Particle Data Compression and Rendering

    cs.GR 2026-07 conditional novelty 6.0 of 10

    ParticleGS uses 3D Gaussian splats to mimic ParaView renderings of 281M-particle data, reaching 30 dB PSNR at 65x compression and rendering at 662 FPS.

  3. You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    YOGO reformulates stochastic 3D Gaussian Splatting into a deterministic budget-aware system and supplies an ultra-dense dataset to enforce physical fidelity over viewpoint interpolation.

  4. Lumina: Real-Time Mobile Neural Rendering by Exploiting Computational Redundancy

    cs.AR 2025-06 conditional novelty 6.0 of 10

    A mobile 3DGS rendering system that shares sorting across frames, caches pixel colors by significant Gaussian IDs, and adds a custom neural rendering unit to reach 4.5x speedup.

  5. Bootstraping Clustering of Gaussians for View-consistent 3D Scene Understanding

    cs.CV 2024-11 conditional novelty 6.0 of 10

    FreeGS bootstraps view-consistent semantics and instance indices in 3D Gaussian Splatting without needing 2D masks.

  6. GSCodec Studio: A Modular Framework for Gaussian Splat Compression

    cs.CV 2025-06 conditional novelty 5.0 of 10

    GSCodec Studio is a modular open-source framework for Gaussian Splat compression, and its composed Static and Dynamic GSCodec pipelines report competitive rate-distortion results against several baselines.

  7. Lifting by Gaussians: A Simple, Fast and Flexible Method for 3D Instance Segmentation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    LBG segments 3D Gaussian Splatting scenes into objects, parts, and subparts by assigning each pixel's maximum-contributing Gaussian a 2D mask ID and merging fragments across frames using geometric and semantic similarity.

  8. Efficient Density Control for 3D Gaussian Splatting

    cs.CV 2024-11 reject novelty 5.0 of 10

    A modified split and pruning scheme for 3D Gaussian Splatting reports higher PSNR with fewer Gaussians, but the manuscript itself says the version is abandoned.

  9. From Coarse to Fine: Learnable Discrete Wavelet Transforms for Efficient 3D Gaussian Splatting

    cs.CV 2025-06 conditional novelty 4.0 of 10

    AutoOpti3DGS uses learnable discrete wavelet transforms on input images to train 3DGS from coarse to fine, reducing peak Gaussian counts by about 18 to 23 percent with modest quality trade-offs.

  10. SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians

    cs.CV 2024-12 conditional novelty 4.0 of 10

    Using three orthogonal spherical Gaussians plus low-degree spherical harmonics for color reduces 3D-GS storage by about 47% and speeds rendering by 1.4x to 1.5x with roughly unchanged PSNR.

  11. Pushing Rendering Boundaries: Hard Gaussian Splatting

    cs.CV 2024-12

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