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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
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 6 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. 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.

  6. 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.

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