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Compact 3D Scene Representation via Self-Organizing Gaussian Grids

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arxiv 2312.13299 v2 pith:R57NSYSJ submitted 2023-12-19 cs.CV

Wieland Morgenstern , Florian Barthel , Anna Hilsmann , Peter Eisert This is my paper · ORCID
classification cs.CV
keywords gaussianparametersscenegridscenescompactduringensuring
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3D Gaussian Splatting has recently emerged as a highly promising technique for modeling of static 3D scenes. In contrast to Neural Radiance Fields, it utilizes efficient rasterization allowing for very fast rendering at high-quality. However, the storage size is significantly higher, which hinders practical deployment, e.g. on resource constrained devices. In this paper, we introduce a compact scene representation organizing the parameters of 3D Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a drastic reduction in storage requirements without compromising visual quality during rendering. Central to our idea is the explicit exploitation of perceptual redundancies present in natural scenes. In essence, the inherent nature of a scene allows for numerous permutations of Gaussian parameters to equivalently represent it. To this end, we propose a novel highly parallel algorithm that regularly arranges the high-dimensional Gaussian parameters into a 2D grid while preserving their neighborhood structure. During training, we further enforce local smoothness between the sorted parameters in the grid. The uncompressed Gaussians use the same structure as 3DGS, ensuring a seamless integration with established renderers. Our method achieves a reduction factor of 17x to 42x in size for complex scenes with no increase in training time, marking a substantial leap forward in the domain of 3D scene distribution and consumption. Additional information can be found on our project page: https://fraunhoferhhi.github.io/Self-Organizing-Gaussians/

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Forward citations

Cited by 9 Pith papers

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

  1. 4DHumanDiff: Direct Text-to-4DGS Generation for Consistent 360-Degree Dynamic Humans

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A diffusion model trained on 60,000 fitted 4D Gaussian Splatting human clips generates text-prompted, view-consistent dynamic humans directly in 4D, over 10x faster than video-first pipelines.

  2. AtlasLC: Fast Codec-Ready Compression of Object-Centric 3D Gaussian Splatting

    cs.GR 2026-07 conditional novelty 6.0 of 10

    A training-free pipeline prunes object-centric 3D Gaussian splats by local competition and packs them into deterministic codec-ready atlases, cutting preparation time and payload with modest quality loss.

  3. Virtual Memory for 3D Gaussian Splatting

    cs.GR 2025-06 conditional novelty 6.0 of 10

    A proxy-mesh visibility buffer with page streaming and level of detail lets 3D Gaussian Splatting render scenes larger than GPU memory while culling occluded Gaussians.

  4. FlexGS: Train Once, Deploy Everywhere with Many-in-One Flexible 3D Gaussian Splatting

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A single 3DGS model with a learned Gaussian selector and transform field renders at any requested compression ratio without fine-tuning.

  5. GIFStream: 4D Gaussian-based Immersive Video with Feature Stream

    cs.CV 2025-05 conditional novelty 6.0 of 10

    A 4D Gaussian representation with sparse time-dependent feature streams achieves state-of-the-art rate-distortion performance for immersive video, compressing dynamic scenes to a few megabytes while rendering in real time.

  6. SfM-Free 3D Gaussian Splatting via Hierarchical Training

    cs.CV 2024-12 conditional novelty 6.0 of 10

    A hierarchical, merge-based training strategy with video frame interpolation improves SfM-free 3D Gaussian Splatting on video, reporting 2.25 dB PSNR gain over CF-3DGS on Tanks and Temples.

  7. Steepest Descent Density Control for Compact 3D Gaussian Splatting

    cs.CV 2025-05 conditional novelty 5.0 of 10

    SteepGS splits a 3D Gaussian only when a computed splitting matrix has a negative eigenvalue, placing two half-opacity offspring along the steepest descent direction, achieving about 50% point reduction with comparabl...

  8. HUG: Hierarchical Urban Gaussian Splatting with Block-Based Reconstruction for Large-Scale Aerial Scenes

    cs.GR 2025-04 conditional novelty 5.0 of 10

    HUG combines visibility-based block partitioning with hierarchical neural Gaussians and level-weighted supervision to improve rendering quality and speed for large-scale aerial scenes.

  9. HAC++: Towards 100X Compression of 3D Gaussian Splatting

    cs.CV 2025-01 conditional novelty 4.0 of 10

    A context-modeling and adaptive-quantization pipeline, built on Scaffold-GS plus a binarized hash grid, improves state-of-the-art 3D Gaussian compression, but the average "100x with better fidelity" claim only holds a...

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