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Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians
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Mini-Splatting: Representing Scenes with a Constrained Number of Gaussians
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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}.
Forward citations
Cited by 6 Pith papers
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3D Gaussian Splatting for Scientific Particle Data Compression and Rendering
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.
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You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
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.
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You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
YOGO delivers deterministic budget-controlled 3D Gaussian Splatting that matches or exceeds prior methods on a new ultra-dense multi-sensor indoor benchmark while keeping primitive counts strictly fixed.
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You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenes
YOGO enforces a fixed Gaussian budget during training via a deterministic controller, and the dense Immersion dataset shifts evaluation from sparse-view interpolation to physical fidelity.
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Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training
A dynamic training framework for 3D Gaussian Splatting alternates incremental pruning and adaptive growing of primitives to maintain high rendering quality at up to 80% lower peak memory than standard 3DGS.
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A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
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