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arxiv: 2602.04549 · v2 · submitted 2026-02-04 · 💻 cs.CV

Nix and Fix: Targeting 1000x Compression of 3D Gaussian Splatting with Diffusion Models

Cem Eteke , Enzo Tartaglione This is my paper

Pith reviewed 2026-05-16 07:38 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingcompressiondiffusion modelsnovel view synthesisperceptual qualityone-step distillationrate reduction
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The pith

NiFi compresses 3D Gaussian Splatting to 0.1 MB using diffusion-based artifact restoration while preserving perceptual quality.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

3D Gaussian Splatting enables fast novel-view rendering but produces large files because it stores many explicit Gaussians. The paper introduces NiFi to push compression to extreme levels by first applying heavy rate reduction that creates visible artifacts and then restoring quality with a diffusion model trained specifically on those artifact patterns. The model is distilled into a single forward pass so restoration stays fast enough for practical use. This yields state-of-the-art perceptual scores at rates down to 0.1 MB and roughly 1000 times smaller files than standard 3DGS at matched visual fidelity.

Core claim

The authors show that an artifact-aware diffusion model distilled for one-step restoration can recover perceptual quality after aggressive compression of 3D Gaussian Splatting, reaching state-of-the-art results at rates as low as 0.1 MB and approximately 1000x rate reduction compared with uncompressed 3DGS while keeping comparable perceptual performance.

What carries the argument

Artifact-aware diffusion-based one-step distillation that learns to reverse the specific distortions introduced by heavy 3DGS compression.

If this is right

  • 3D scenes become storable and transmissible at fractions of current sizes while still supporting real-time rendering.
  • Applications in bandwidth-limited settings such as mobile AR or web-based 3D viewers become feasible at high visual fidelity.
  • The output remains a standard 3DGS representation, preserving the original real-time rasterization speed.
  • The approach establishes a new operating point for rate-distortion trade-offs in explicit 3D scene representations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same artifact-specific distillation idea could be tested on other explicit 3D representations such as point clouds or meshes.
  • Adapting the diffusion model to multiple compression codecs might allow a single restoration network to handle varied artifact types.
  • Combining NiFi with learned quantization schedules could push rates even lower before restoration quality begins to degrade.

Load-bearing premise

A diffusion model trained on compression artifact patterns can restore visual quality from highly compressed 3DGS without adding new distortions or losing perceptually important scene details.

What would settle it

Quantitative perceptual metrics or human viewer studies showing that the restored 0.1 MB scenes score lower in quality than the original uncompressed 3DGS or contain new artifacts absent from the source.

read the original abstract

3D Gaussian Splatting (3DGS) revolutionized novel view rendering. Instead of inferring from dense spatial points, as implicit representations do, 3DGS uses sparse Gaussians. This enables real-time performance but increases space requirements, hindering rate-constrained applications. 3DGS compression emerged as a field aimed at alleviating this issue. While impressive progress has been made, at low rates, compression introduces artifacts that degrade visual quality significantly. We introduce NiFi, a method for extreme 3DGS compression through restoration via artifact-aware, diffusion-based one-step distillation. We show that our method achieves state-of-the-art perceptual quality at extremely low rates, down to 0.1 MB, and towards 1000x rate improvement over 3DGS at comparable perceptual performance. Code is available at: https://github.com/ceteke/nifi

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces NiFi, an artifact-aware one-step diffusion distillation method to restore visual quality in highly compressed 3D Gaussian Splatting (3DGS) scenes. It claims state-of-the-art perceptual quality at rates down to 0.1 MB, corresponding to up to 1000x rate improvement over uncompressed 3DGS while maintaining comparable perceptual performance.

Significance. If the restoration reliably recovers scene content without hallucination, the approach could enable 3DGS deployment in severely bandwidth-limited settings such as mobile AR/VR. The artifact-aware training and one-step distillation represent a targeted application of diffusion models to compression artifacts, but the extreme compression regime makes the method inherently generative, which limits its significance unless fidelity to ground truth is demonstrated.

major comments (2)
  1. [Abstract and §4] Abstract and §4 (Experiments): the central 1000x improvement and SOTA perceptual quality claims at 0.1 MB are stated without accompanying quantitative tables, baselines (e.g., prior 3DGS compressors), or metrics (PSNR, LPIPS, user-study scores); this absence prevents verification that perceptual gains are supported by data rather than plausible synthesis.
  2. [§3] §3 (Method): at ~0.1 MB the input 3DGS is severely degraded, so the diffusion prior is likely to dominate; the manuscript must demonstrate that the one-step model recovers true geometry and appearance rather than hallucinating details, because perceptual metrics can improve from plausible but incorrect content, directly undermining the 'comparable perceptual performance' claim.
minor comments (1)
  1. [§3 and §4] Ensure all training hyperparameters, artifact simulation procedure, and evaluation protocols are fully specified so that the GitHub code can be reproduced without ambiguity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of our results and the need to substantiate claims in the extreme compression regime. We will revise the manuscript to include the requested quantitative tables, baselines, and additional fidelity analysis while preserving the core contributions of artifact-aware one-step distillation.

read point-by-point responses

  1. Referee: [Abstract and §4] Abstract and §4 (Experiments): the central 1000x improvement and SOTA perceptual quality claims at 0.1 MB are stated without accompanying quantitative tables, baselines (e.g., prior 3DGS compressors), or metrics (PSNR, LPIPS, user-study scores); this absence prevents verification that perceptual gains are supported by data rather than plausible synthesis.

    Authors: We agree that the abstract and §4 would benefit from explicit supporting data. In the revised manuscript we will insert a new table in §4 reporting PSNR, LPIPS, and user-study preference scores for NiFi versus prior 3DGS compressors (e.g., recent quantization and pruning baselines) at matched low bit-rates. The 1000× figure is the ratio of typical uncompressed 3DGS sizes (50–200 MB) to the 0.1 MB NiFi output; we will state this calculation explicitly and cite the exact baseline sizes used. These additions will allow direct verification of the perceptual-quality claims. revision: yes

  2. Referee: [§3] §3 (Method): at ~0.1 MB the input 3DGS is severely degraded, so the diffusion prior is likely to dominate; the manuscript must demonstrate that the one-step model recovers true geometry and appearance rather than hallucinating details, because perceptual metrics can improve from plausible but incorrect content, directly undermining the 'comparable perceptual performance' claim.

    Authors: We acknowledge the risk of hallucination at such low rates. Our artifact-aware training objective explicitly penalizes deviation from the degraded input on known scene elements, yet the diffusion prior necessarily supplies missing detail. In the revision we will add geometric-consistency experiments (Chamfer distance on recovered point clouds, depth-map error, and normal consistency) comparing restored scenes to ground-truth renders. These metrics will be reported alongside the existing perceptual scores to show that overall structure is recovered rather than arbitrarily invented, thereby supporting the “comparable perceptual performance” statement with evidence beyond LPIPS alone. revision: partial

Circularity Check

0 steps flagged

No circularity: method introduces external diffusion restoration without self-referential derivation

full rationale

The paper presents NiFi as a new artifact-aware one-step diffusion distillation technique applied to already-compressed 3DGS inputs. No equations, fitted parameters, or predictions are shown that reduce the claimed 1000x compression or perceptual quality gains to the method's own inputs by construction. The central claim rests on empirical restoration performance rather than any self-definition, fitted-input renaming, or load-bearing self-citation chain. The derivation chain is self-contained against external benchmarks and does not invoke uniqueness theorems or ansatzes from prior author work.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that the diffusion restoration generalizes across scenes and compression levels.

pith-pipeline@v0.9.0 · 5451 in / 1101 out tokens · 28215 ms · 2026-05-16T07:38:22.608701+00:00 · methodology

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Reference graph

Works this paper leans on

33 extracted references · 33 canonical work pages · 2 internal anchors

  1. [1]

    INTRODUCTION Utilizing 3D Gaussian Splatting (3DGS) for novel-view ren- dering has emerged as an alternative to implicit neural radi- ance models [1]. Instead of the dense prediction scheme of the latter, i.e., estimating color and opacity from given points in 3D space, 3DGS fits sparse Gaussians with attributes, such as color, scale, and position, in rel...

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  2. [2]

    used 3DGS to represent a scene with a set of sparse GaussiansG:={G i(x)}L i=1, i.e., primitives [1]

    RELATED WORK 3D Gaussian Splatting.Kerbl et al. used 3DGS to represent a scene with a set of sparse GaussiansG:={G i(x)}L i=1, i.e., primitives [1]. In this formulation, each primitive’s mean and covariance represent its three-dimensional geometry: the mean encodes position, while the covariance captures rotation and scale. Novel view rendering is achieve...

    work page

  3. [3]

    We aim to restore these artifacts by formulating a blind image restoration problem, enabling 3DGS compression at extreme-low rates

    METHODOLOGY Information loss at the 3D representation distorts both the ge- ometry and appearance, thus compressing 3DGS, especially at extremely low rates, produces complex artifacts. We aim to restore these artifacts by formulating a blind image restoration problem, enabling 3DGS compression at extreme-low rates. Artifact Synthesisstep shown in Fig. 2 g...

    work page

  4. [4]

    Implementation Details We used the DL3DV dataset with10 3 scenes to create the simulated 3DGS compression artifacts dataset [27]

    EXPERIMENTS 4.1. Implementation Details We used the DL3DV dataset with10 3 scenes to create the simulated 3DGS compression artifacts dataset [27]. We set the minimum number of primitives for pruningc min = 4096 and selected the number of primitives at three rates as de- scribed in Sec. 3. We trained the two low-rank adapters,ϕ − andϕ + with rank 64, on th...

    work page

  5. [5]

    We also demon- strated that mapping to an immediate point on the diffusion trajectory significantly improves perceptual performance

    CONCLUSION We introduced NiFi, an extreme 3DGS compression method that extends variational diffusion distillation for restoring 3DGS compression artifacts, enabling 3DGS compression at extremely low rates, reaching0.110MB. We also demon- strated that mapping to an immediate point on the diffusion trajectory significantly improves perceptual performance. O...

    work page

  6. [6]

    3d gaussian splatting for real-time radiance field rendering.,

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis, “3d gaussian splatting for real-time radiance field rendering.,”ACM Trans. Graph., vol. 42, no. 4, pp. 139– 1, 2023

    work page 2023

  7. [7]

    Compression in 3d gaussian splatting: A survey of methods, trends, and future directions,

    Muhammad Salman Ali, Chaoning Zhang, Marco Cagnazzo, Giuseppe Valenzise, Enzo Tartaglione, and Sung-Ho Bae, “Compression in 3d gaussian splatting: A survey of methods, trends, and future directions,”arXiv preprint arXiv:2502.19457, 2025

    work page arXiv 2025

  8. [8]

    Hac++: Towards 100x compression of 3d gaus- sian splatting,

    Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, and Jianfei Cai, “Hac++: Towards 100x compression of 3d gaus- sian splatting,”IEEE TPAMI, 2025

    work page 2025

  9. [9]

    High-resolution image synthesis with latent diffusion models,

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer, “High-resolution image synthesis with latent diffusion models,” inCVPR, 2022, pp. 10684– 10695

    work page 2022

  10. [10]

    One-step diffusion with distribution matching distillation,

    Tianwei Yin, Micha ¨el Gharbi, Richard Zhang, Eli Shecht- man, Fredo Durand, William T Freeman, and Taesung Park, “One-step diffusion with distribution matching distillation,” in CVPR, 2024, pp. 6613–6623

    work page 2024

  11. [11]

    Bm3d frames and variational image deblurring,

    Aram Danielyan, Vladimir Katkovnik, and Karen Egiazarian, “Bm3d frames and variational image deblurring,”IEEE TIP, vol. 21, no. 4, pp. 1715–1728, 2011

    work page 2011

  12. [12]

    Swinir: Image restoration using swin transformer,

    Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte, “Swinir: Image restoration using swin transformer,” inICCV, 2021, pp. 1833–1844

    work page 2021

  13. [13]

    Diff- bir: Toward blind image restoration with generative diffusion prior,

    Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Yu Qiao, Wanli Ouyang, and Chao Dong, “Diff- bir: Toward blind image restoration with generative diffusion prior,” inECCV. Springer, 2024, pp. 430–448

    work page 2024

  14. [14]

    Difix3d+: Improving 3d reconstructions with single-step diffusion models,

    Jay Zhangjie Wu, Yuxuan Zhang, Haithem Turki, Xuanchi Ren, Jun Gao, Mike Zheng Shou, Sanja Fidler, Zan Gojcic, and Huan Ling, “Difix3d+: Improving 3d reconstructions with single-step diffusion models,” inCVPR, 2025, pp. 26024– 26035

    work page 2025

  15. [15]

    Compgs: Smaller and faster gaussian splatting with vector quantization,

    KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Ab- basi Koohpayegani, and Hamed Pirsiavash, “Compgs: Smaller and faster gaussian splatting with vector quantization,” in ECCV. Springer, 2024, pp. 330–349

    work page 2024

  16. [16]

    Efficientgs: Stream- lining gaussian splatting for large-scale high-resolution scene representation,

    Wenkai Liu, Tao Guan, Bin Zhu, Luoyuan Xu, Zikai Song, Dan Li, Yuesong Wang, and Wei Yang, “Efficientgs: Stream- lining gaussian splatting for large-scale high-resolution scene representation,”IEEE MultiMedia, 2025

    work page 2025

  17. [17]

    Gode: Gaussians on demand for progressive level of detail and scalable compres- sion,

    Francesco Di Sario, Riccardo Renzulli, Marco Grangetto, Ak- ihiro Sugimoto, and Enzo Tartaglione, “Gode: Gaussians on demand for progressive level of detail and scalable compres- sion,”arXiv preprint arXiv:2501.13558, 2025

    work page arXiv 2025

  18. [18]

    Lightgaussian: Unbounded 3d gaus- sian compression with 15x reduction and 200+ fps,

    Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang, et al., “Lightgaussian: Unbounded 3d gaus- sian compression with 15x reduction and 200+ fps,”NeurIPS, vol. 37, pp. 140138–140158, 2024

    work page 2024

  19. [19]

    Ea- gles: Efficient accelerated 3d gaussians with lightweight en- codings,

    Sharath Girish, Kamal Gupta, and Abhinav Shrivastava, “Ea- gles: Efficient accelerated 3d gaussians with lightweight en- codings,” inECCV. Springer, 2024, pp. 54–71

    work page 2024

  20. [20]

    Scaffold-gs: Structured 3d gaus- sians for view-adaptive rendering,

    Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai, “Scaffold-gs: Structured 3d gaus- sians for view-adaptive rendering,” inCVPR, 2024, pp. 20654– 20664

    work page 2024

  21. [21]

    Hemgs: A hybrid entropy model for 3d gaussian splatting data compression,

    Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, and Dong Xu, “Hemgs: A hybrid entropy model for 3d gaussian splatting data compression,”arXiv preprint arXiv:2411.18473, 2024

    work page arXiv 2024

  22. [22]

    Compression of 3d gaussian splatting with optimized feature planes and standard video codecs,

    Soonbin Lee, Fangwen Shu, Yago Sanchez, Thomas Schierl, and Cornelius Hellge, “Compression of 3d gaussian splatting with optimized feature planes and standard video codecs,” in ICCV, October 2025, pp. 25496–25505

    work page 2025

  23. [23]

    Real-esrgan: Training real-world blind super-resolution with pure synthetic data,

    Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan, “Real-esrgan: Training real-world blind super-resolution with pure synthetic data,” inICCV, 2021, pp. 1905–1914

    work page 2021

  24. [24]

    Diffusion models for image restoration and enhancement: a comprehensive sur- vey,

    Xin Li, Yulin Ren, Xin Jin, Cuiling Lan, Xingrui Wang, Wen- jun Zeng, Xinchao Wang, and Zhibo Chen, “Diffusion models for image restoration and enhancement: a comprehensive sur- vey,”IJCV, vol. 133, no. 11, pp. 8078–8108, 2025

    work page 2025

  25. [25]

    Tsd- sr: One-step diffusion with target score distillation for real- world image super-resolution,

    Linwei Dong, Qingnan Fan, Yihong Guo, Zhonghao Wang, Qi Zhang, Jinwei Chen, Yawei Luo, and Changqing Zou, “Tsd- sr: One-step diffusion with target score distillation for real- world image super-resolution,” inCVPR, 2025, pp. 23174– 23184

    work page 2025

  26. [26]

    Gs- fix3d: Diffusion-guided repair of novel views in gaussian splat- ting,

    Jiaxin Wei, Stefan Leutenegger, and Simon Schaefer, “Gs- fix3d: Diffusion-guided repair of novel views in gaussian splat- ting,”arXiv preprint arXiv:2508.14717, 2025

    work page arXiv 2025

  27. [27]

    Leveraging learned image prior for 3d gaussian compression,

    Seungjoo Shin, Jaesik Park, and Sunghyun Cho, “Leveraging learned image prior for 3d gaussian compression,” inICCV, 2025, pp. 3047–3056

    work page 2025

  28. [28]

    Mip-nerf 360: Unbounded anti- aliased neural radiance fields,

    Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srini- vasan, and Peter Hedman, “Mip-nerf 360: Unbounded anti- aliased neural radiance fields,” inCVPR, 2022, pp. 5470–5479

    work page 2022

  29. [29]

    Tanks and temples: Benchmarking large-scale scene reconstruction,

    Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun, “Tanks and temples: Benchmarking large-scale scene reconstruction,”ACM Trans. on Graph., vol. 36, no. 4, pp. 1–13, 2017

    work page 2017

  30. [30]

    Deep blending for free-viewpoint image-based rendering,

    Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow, “Deep blending for free-viewpoint image-based rendering,”ACM Trans. on Graph., vol. 37, no. 6, pp. 1–15, 2018

    work page 2018

  31. [31]

    Scaling rectified flow transformers for high-resolution image synthesis,

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim En- tezari, Jonas M¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al., “Scaling rectified flow transformers for high-resolution image synthesis,” inICML, 2024

    work page 2024

  32. [32]

    Dl3dv-10k: A large-scale scene dataset for deep learning- based 3d vision,

    Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, et al., “Dl3dv-10k: A large-scale scene dataset for deep learning- based 3d vision,” inCVPR, 2024, pp. 22160–22169

    work page 2024

  33. [33]

    Qwen2.5-VL Technical Report

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Hu- men Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jian- qiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin, “Qwen2.5-vl tech- nical re...

    work page internal anchor Pith review Pith/arXiv arXiv 2025