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arxiv: 2606.11782 · v1 · pith:KW2WSXHUnew · submitted 2026-06-10 · 💻 cs.CV

Seeing What Matters: Perceptual Wrapper with Common Randomness for 3D Gaussian Splatting

He-Bi Yang , Jing-Zhong Chen , Yen-Kuan Ho , Sang NguyenQuang , Fan-Yi Hsu , Yun-Yu Lee , Jui-Chiu Chiang , Wen-Hsiao Peng This is my paper

Pith reviewed 2026-06-27 10:07 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingperceptual wrappertexture synthesisWasserstein Distortionneural renderingrate-distortion optimization
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The pith

A lightweight network conditioned on random noise enhances perceptual textures in 3D Gaussian Splatting outputs.

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

The paper introduces a 2D perceptual wrapper for 3D Gaussian Splatting that uses a synthesis network to add high-frequency textures. The network is conditioned on pseudo-random Gaussian noise and trained using Wasserstein Distortion to match local feature statistics instead of exact pixel values. This approach works as a plug-and-play addition to existing 3DGS methods, including those optimized for memory or rate-distortion. Subjective and objective tests show it delivers better visual quality even as file sizes decrease significantly.

Core claim

We propose a versatile 2D perceptual wrapper that enhances the rendered outputs of existing 3DGS representations in a content- and view-dependent manner. Our method leverages a lightweight synthesis network conditioned on pseudo-random Gaussian noise to synthesize perceptually plausible textures. Supervised by Wasserstein Distortion, the network learns to match local feature statistics rather than strictly enforcing pixel-wise reconstruction fidelity, effectively mitigating the blurriness inherent in standard frameworks.

What carries the argument

Lightweight synthesis network conditioned on pseudo-random Gaussian noise and supervised by Wasserstein Distortion to match local feature statistics.

If this is right

  • The wrapper improves perceptual quality for vanilla 3DGS representations.
  • It enables memory-constrained 3DGS to achieve higher quality at reduced model sizes.
  • RDO-optimized 3DGS benefits from further size reductions with maintained or improved perceptual quality.
  • The method mitigates blurriness without requiring changes to the underlying 3DGS pipeline.

Where Pith is reading between the lines

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

  • This technique could extend to other view synthesis methods that suffer from texture loss.
  • Using common randomness may help maintain consistency in other multi-view rendering tasks.
  • Exploring alternative conditioning signals beyond Gaussian noise could further improve results.

Load-bearing premise

That conditioning a lightweight synthesis network on pseudo-random Gaussian noise will produce content- and view-dependent textures that remain consistent across views and do not introduce new artifacts when applied to existing 3DGS representations.

What would settle it

Observing view-inconsistent artifacts or new visual defects in rendered sequences after applying the wrapper would disprove the method's reliability.

Figures

Figures reproduced from arXiv: 2606.11782 by Fan-Yi Hsu, He-Bi Yang, Jing-Zhong Chen, Jui-Chiu Chiang, Sang NguyenQuang, Wen-Hsiao Peng, Yen-Kuan Ho, Yun-Yu Lee.

Figure 1
Figure 1. Figure 1: Comparison of 3DGS frameworks. (a) Traditional methods often struggle to reproduce high-frequency textures due to suboptimal distortion metrics. (b) Recently, Perceptual-GS [46] learns Gaussian primitives by sensitivity guided densification to im￾prove perceptual quality. (c) Our plug-and-play perceptual wrapper integrates a tex￾ture synthesis network conditioned on Plücker embeddings and pseudo-random Gau… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of 3DGS related works, their training objectives, and their relation to our proposed method. synthesis network adopts a lightweight implementation. Finally, our perceptual wrapper is trained end-to-end along with the core 3DGS representation using the WD loss to optimize perceptual quality. The main contributions of this work are three-fold: (1) it marks the first at￾tempt to integrate image-based… view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of Wasserstein Distortion. The distortion is computed as the 2- Wasserstein divergence between pairs of local Gaussian statistics in the feature space. (commonly a combination of L1 and SSIM [41], referred to as L3DGS−D) to￾gether with a method-specific regularization loss LReg and optionally a rate term Lrate for RDO methods. However, L3DGS−D prioritizes pixel-wise signal fidelity, which is i… view at source ↗
Figure 4
Figure 4. Figure 4: Overview of the proposed framework. The perceptual wrapper uses a synthesis network to refine the rendered image I vj base in a content- and view-dependent manner. The whole pipeline is optimized via minimizing a Wasserstein Distortion loss LWD across training views. directly in the 3DGS domain highly challenging. The requirement of novel view synthesis further demands that texture synthesis adapt to viewp… view at source ↗
Figure 5
Figure 5. Figure 5: Rate-distortion performance across RDO and non-RDO methods [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative results of our perceptual wrapper (PW) integrated into RDO meth￾ods, CAT-3DGS Pro∗ [43] and HAC++ [8]. 4.2 Quantitative and Qualitative Results Comparisons with RDO-based 3DGS. Fig. 5a reports the results of our subjective study after integrating our perceptual wrapper into RDO-based 3DGS methods, including CAT-3DGS Pro∗ and HAC++. Across all evaluated rate points and baseline methods, our appr… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative results of our perceptual wrapper (PW) integrated into non-RDO methods, 3DGS [18], OMG [22] and Perceptual-GS [46]. In contrast, with our perceptual wrapper, the model exhibits a significant reduc￾tion in Gaussian density in these regions. This confirms that by offloading the reconstruction of high-frequency details to our synthesis network, our perceptual wrapper relieves the 3DGS representati… view at source ↗
Figure 8
Figure 8. Figure 8: Comparison of residual maps R vj (the second row) with and without Plücker embeddings. The first row shows their corresponding final outputs [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Visualization of 3DGS point clouds with and without our perceptual wrapper. time from 85 to 304 minutes due to the extra computation required for WD evaluation, which involves multi-scale VGG feature extraction at each iteration. Likewise, the average rendering speed decreases from 66 FPS to 31 FPS, as the synthesis network introduces an additional forward pass for each rendered im￾age. We consider these c… view at source ↗
read the original abstract

While 3D Gaussian Splatting (3DGS) achieves impressive real-time rendering, it frequently struggles to synthesize high-frequency textures, a limitation heavily exacerbated in memory-constrained and rate-distortion-optimized (RDO) pipelines. To address this, we propose a versatile 2D perceptual wrapper that enhances the rendered outputs of existing 3DGS representations in a content- and view-dependent manner. Our method leverages a lightweight synthesis network conditioned on pseudo-random Gaussian noise to synthesize perceptually plausible textures. Supervised by Wasserstein Distortion, the network learns to match local feature statistics rather than strictly enforcing pixel-wise reconstruction fidelity, effectively mitigating the blurriness inherent in standard frameworks. We demonstrate the broad applicability of our plug-and-play approach across vanilla, memory-constrained, and RDO 3DGS methods. Comprehensive subjective and objective experiments confirm that our method significantly improves over existing baselines, yielding superior perceptual quality at sharply reduced file or model sizes.

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 / 0 minor

Summary. The manuscript proposes a 2D perceptual wrapper for 3D Gaussian Splatting that applies a lightweight synthesis network conditioned on pseudo-random Gaussian noise (with common randomness) to rendered outputs. The network is supervised solely by Wasserstein Distortion on local feature statistics to synthesize high-frequency textures in a content- and view-dependent manner without enforcing pixel-wise fidelity. The approach is presented as a plug-and-play enhancement applicable to vanilla, memory-constrained, and RDO 3DGS pipelines, with the abstract asserting that comprehensive subjective and objective experiments demonstrate superior perceptual quality at sharply reduced file or model sizes.

Significance. If the shared pseudo-random noise successfully enforces view-consistent textures without introducing flickering or ghosting, the wrapper would provide a practical, low-overhead method for perceptual enhancement of existing 3DGS representations, particularly valuable in rate-distortion-optimized and memory-constrained regimes where increasing model size is undesirable.

major comments (2)
  1. [Abstract] Abstract: the central claim that 'comprehensive subjective and objective experiments confirm that our method significantly improves over existing baselines' is unsupported by any quantitative results, metrics, datasets, ablation studies, or error analysis, rendering the asserted superiority unverifiable from the manuscript.
  2. [Abstract] Abstract: the method's reliance on conditioning a synthesis network on pseudo-random Gaussian noise with common randomness to produce view-consistent textures lacks any described mechanism (e.g., 3D-position-dependent noise generation or explicit multi-view consistency loss) that would guarantee identical high-frequency output for the same surface point across camera angles, directly undermining the no-new-artifacts assumption required for the plug-and-play claim.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the two major comments on the abstract point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that 'comprehensive subjective and objective experiments confirm that our method significantly improves over existing baselines' is unsupported by any quantitative results, metrics, datasets, ablation studies, or error analysis, rendering the asserted superiority unverifiable from the manuscript.

    Authors: The full manuscript contains Section 4 with objective metrics (LPIPS, FID), subjective user studies, ablation studies, and results on standard datasets including Mip-NeRF 360 and Tanks & Temples. The abstract is a high-level summary of those findings. We will revise the abstract to explicitly name the key metrics, datasets, and improvement margins so the claim is directly verifiable. revision: yes

  2. Referee: [Abstract] Abstract: the method's reliance on conditioning a synthesis network on pseudo-random Gaussian noise with common randomness to produce view-consistent textures lacks any described mechanism (e.g., 3D-position-dependent noise generation or explicit multi-view consistency loss) that would guarantee identical high-frequency output for the same surface point across camera angles, directly undermining the no-new-artifacts assumption required for the plug-and-play claim.

    Authors: Section 3.2 explains that the pseudo-random Gaussian noise is generated deterministically from each 3D Gaussian's position and a fixed global seed, ensuring identical noise input (and thus identical high-frequency synthesis) for the same surface point regardless of viewpoint. No explicit multi-view loss is used because the 3D-position conditioning already enforces consistency. We will add a clarifying paragraph and pseudocode in the revised manuscript to make this mechanism more explicit. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical plug-in method with external supervision

full rationale

The paper presents a lightweight synthesis network as a plug-and-play wrapper for 3DGS outputs, conditioned on pseudo-random noise and trained to match local feature statistics via Wasserstein Distortion. No equations, derivations, or predictions are claimed that reduce by construction to fitted parameters or self-citations. Improvements are asserted via comprehensive subjective/objective experiments on vanilla, memory-constrained, and RDO 3DGS baselines. The approach is self-contained against external benchmarks with no load-bearing self-citation chains or self-definitional steps visible in the abstract or described method.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; the method implicitly assumes that Wasserstein feature matching produces view-consistent textures and that the synthesis network generalizes across 3DGS variants without additional constraints.

pith-pipeline@v0.9.1-grok · 5730 in / 959 out tokens · 27688 ms · 2026-06-27T10:07:15.500482+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

46 extracted references · 15 canonical work pages

  1. [1]

    compression.cc/(2025), last accessed 2026/03/04 Seeing What Matters 15

    7th challenge on learned image compression (CLIC 2025).https://clic2025. compression.cc/(2025), last accessed 2026/03/04 Seeing What Matters 15

    2025

  2. [2]

    arXiv preprint arXiv:2406.18214 (2024)

    Ali, M.S., Qamar, M., Bae, S.H., Tartaglione, E.: Trimming the fat: Efficient com- pression of 3d gaussian splats through pruning. arXiv preprint arXiv:2406.18214 (2024)

    arXiv 2024

  3. [3]

    IEEE Transactions on Image Processing32, 5046–5059 (2023).https://doi.org/10.1109/TIP.2023

    Ameur, Z., Hamidouche, W., François, E., Radosavljević, M., Menard, D., De- marty, C.H.: Deep-based film grain removal and synthesis. IEEE Transactions on Image Processing32, 5046–5059 (2023).https://doi.org/10.1109/TIP.2023. 3308726

    work page doi:10.1109/tip.2023 2023

  4. [4]

    RLAIF-V: open-source AI feedback leads to super GPT-4V trustworthiness

    Ballé, J., Versari, L., Dupont, E., Kim, H., Bauer, M.: Good, cheap, and fast: Overfitted image compression with wasserstein distortion. In: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 23259– 23268 (2025).https://doi.org/10.1109/CVPR52734.2025.02166

    work page doi:10.1109/cvpr52734.2025.02166 2025

  5. [5]

    Zickler, Jonathan T

    Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: 2022 IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR). pp. 5460–5469 (2022). https://doi.org/10.1109/CVPR52688.2022.00539

    work page doi:10.1109/cvpr52688.2022.00539 2022

  6. [6]

    Journal of Computational and Graphical Statistics21(1), 174–196 (2010)

    Caron, F., Doucet, A.: Efficient bayesian inference for generalized bradley–terry models. Journal of Computational and Graphical Statistics21(1), 174–196 (2010)

    2010

  7. [7]

    In: European Conference on Computer Vision (2024)

    Chen,Y.,Wu,Q.,Lin,W.,Harandi,M.,Cai,J.:Hac:Hash-gridassistedcontextfor 3d gaussian splatting compression. In: European Conference on Computer Vision (2024)

    2024

  8. [8]

    GUS-IR: Gaussian Splatting With Unified Shading for Inverse Rendering .IEEE Transactions on Pattern Analysis & Machine Intelligence, 47(10):8364–8378, October 2025

    Chen, Y., Wu, Q., Lin, W., Harandi, M., Cai, J.: Hac++: Towards 100x compres- sion of 3d gaussian splatting. IEEE Transactions on Pattern Analysis and Machine Intelligence47(11), 10210–10226 (2025).https://doi.org/10.1109/TPAMI.2025. 3594066

    work page doi:10.1109/tpami.2025 2025

  9. [9]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Chen, Y., Jiang, J., Jiang, K., Tang, X., Li, Z., Liu, X., Nie, Y.: Dashgaussian: Optimizing 3d gaussian splatting in 200 seconds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11146– 11155 (June 2025)

    2025

  10. [10]

    Evangelidis and Emmanouil Z

    Ding, K., Ma, K., Wang, S., Simoncelli, E.P.: Image quality assessment: Unify- ing structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence44(5), 2567–2581 (2022).https://doi.org/10.1109/TPAMI. 2020.3045810

    work page doi:10.1109/tpami 2022

  11. [11]

    Advances in neural information processing systems37, 140138–140158 (2024)

    Fan, Z., Wang, K., Wen, K., Zhu, Z., Xu, D., Wang, Z.: Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps. Advances in neural information processing systems37, 140138–140158 (2024)

    2024

  12. [12]

    In: European conference on computer vision

    Fang,G.,Wang,B.:Mini-splatting:Representingsceneswithaconstrainednumber of gaussians. In: European conference on computer vision. pp. 165–181. Springer (2024)

    2024

  13. [13]

    Proceedings of the ACM on Computer Graphics and Interactive Techniques8(1), 1–21 (2025)

    Franke, L., Fink, L., Stamminger, M.: Vr-splatting: Foveated radiance field ren- dering via 3d gaussian splatting and neural points. Proceedings of the ACM on Computer Graphics and Interactive Techniques8(1), 1–21 (2025)

    2025

  14. [14]

    In: European Conference on Computer Vision

    Girish, S., Gupta, K., Shrivastava, A.: Eagles: Efficient accelerated 3d gaussians with lightweight encodings. In: European Conference on Computer Vision. pp. 54–71. Springer (2024)

    2024

  15. [15]

    RLAIF-V: open-source AI feedback leads to super GPT-4V trustworthiness

    Hanson, A., Tu, A., Lin, G., Singla, V., Zwicker, M., Goldstein, T.: Speedy- splat: Fast 3d gaussian splatting with sparse pixels and sparse primitives. In: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 21537–21546 (2025).https://doi.org/10.1109/CVPR52734.2025.02006

    work page doi:10.1109/cvpr52734.2025.02006 2025

  16. [16]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Hanson, A., Tu, A., Singla, V., Jayawardhana, M., Zwicker, M., Goldstein, T.: Pup 3d-gs: Principled uncertainty pruning for 3d gaussian splatting. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 5949–5958 (2025) 16 H.-B. Yang et al

    2025

  17. [17]

    ACM Trans

    Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G., Brostow, G.: Deep blending for free-viewpoint image-based rendering. ACM Trans. Graph.37(6) (Dec 2018)

    2018

  18. [18]

    ACM Transactions on Graphics42(4) (July 2023),https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics42(4) (July 2023),https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/

    2023

  19. [19]

    11 Stage-wise Distortion–Perception Traversal in Zero-shot Inverse Problems with Diffusion Models Xu, X

    Kim, H., Bauer, M., Theis, L., Schwarz, J.R., Dupont, E.: C3: High-performance and low-complexity neural compression from a single image or video. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9347–9358 (2024).https://doi.org/10.1109/CVPR52733.2024.00893

    work page doi:10.1109/cvpr52733.2024.00893 2024

  20. [20]

    ACM Trans

    Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph.36(4) (Jul 2017)

    2017

  21. [21]

    Bokhovkin, S

    Ladune, T., Philippe, P., Henry, F., Clare, G., Leguay, T.: Cool-chic: Coordinate- based low complexity hierarchical image codec. In: 2023 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 13469–13476 (2023).https://doi. org/10.1109/ICCV51070.2023.01243

    work page doi:10.1109/iccv51070.2023.01243 2023

  22. [22]

    In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025), https://openreview.net/forum?id=GMiC4ccyHn

    Lee, J.C., Ko, J.H., Park, E.: Optimized minimal 3d gaussian splatting. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems (2025), https://openreview.net/forum?id=GMiC4ccyHn

    2025

  23. [23]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Lee, J.C., Rho, D., Sun, X., Ko, J.H., Park, E.: Compact 3d gaussian representation for radiance field. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21719–21728 (2024)

    2024

  24. [24]

    Lin,W.,Feng,Y.,Zhu,Y.:Metasapiens:Real-timeneuralrenderingwithefficiency- awarepruningandacceleratedfoveatedrendering.In:Proceedingsofthe30thACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1. pp. 669–682 (2025)

    2025

  25. [25]

    arXiv preprint arXiv:2411.18473 (2024)

    Liu, L., Chen, Z., Jiang, W., Wang, W., Xu, D.: Hemgs: A hybrid entropy model for 3d gaussian splatting data compression. arXiv preprint arXiv:2411.18473 (2024)

    arXiv 2024

  26. [26]

    Liu, X., Wu, X., Zhang, P., Wang, S., Li, Z., Kwong, S.: Compgs: Efficient 3d scene representation viacompressedgaussiansplatting.In:Proceedingsofthe32ndACM International Conference on Multimedia. pp. 2936–2944 (2024)

    2024

  27. [27]

    11 Stage-wise Distortion–Perception Traversal in Zero-shot Inverse Problems with Diffusion Models Xu, X

    Lu, T., Yu, M., Xu, L., Xiangli, Y., Wang, L., Lin, D., Dai, B.: Scaffold-gs: Struc- tured 3d gaussians for view-adaptive rendering. In: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 20654–20664 (2024). https://doi.org/10.1109/CVPR52733.2024.01952

    work page doi:10.1109/cvpr52733.2024.01952 2024

  28. [28]

    mabyduck

    Mabyduck: Mabyduck.https : / / www . mabyduck . com(2025), last accessed 2026/03/04

    2025

  29. [29]

    In: SIGGRAPH Asia 2024 Conference Papers

    Mallick, S.S., Goel, R., Kerbl, B., Steinberger, M., Carrasco, F.V., De La Torre, F.: Taming 3dgs: High-quality radiance fields with limited resources. In: SIGGRAPH Asia 2024 Conference Papers. pp. 1–11 (2024)

    2024

  30. [30]

    In: 2018 Data Compression Conference

    Norkin, A., Birkbeck, N.: Film grain synthesis for av1 video codec. In: 2018 Data Compression Conference. pp. 3–12 (2018).https://doi.org/10.1109/DCC.2018. 00008

    work page doi:10.1109/dcc.2018 2018

  31. [31]

    Proceedings of the ACM on Computer Graphics and Interactive Techniques7(1), 1–17 (2024)

    Papantonakis, P., Kopanas, G., Kerbl, B., Lanvin, A., Drettakis, G.: Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques7(1), 1–17 (2024)

    2024

  32. [32]

    In: 2024 58th Annual Conference on Information Sciences and Systems (CISS)

    Qiu, Y., Wagner, A.B., Ballé, J., Theis, L.: Wasserstein distortion: Unifying fidelity and realism. In: 2024 58th Annual Conference on Information Sciences and Systems (CISS). pp. 1–6 (2024).https://doi.org/10.1109/CISS59072.2024.10480168 Seeing What Matters 17

    work page doi:10.1109/ciss59072.2024.10480168 2024

  33. [33]

    1–15 (2025).https://doi.org/ 10.1109/TPAMI.2025.3568201

    Ren, K., Jiang, L., Lu, T., Yu, M., Xu, L., Ni, Z., Dai, B.: Octree-gs: Towards consistentreal-timerenderingwithlod-structured3dgaussians.IEEETransactions on Pattern Analysis and Machine Intelligence pp. 1–15 (2025).https://doi.org/ 10.1109/TPAMI.2025.3568201

    work page doi:10.1109/tpami.2025.3568201 2025

  34. [34]

    arXiv preprint arXiv:2511.04283 (2025)

    Ren, S., Wen, T., Fang, Y., Lu, B.: Fastgs: Training 3d gaussian splatting in 100 seconds. arXiv preprint arXiv:2511.04283 (2025)

    arXiv 2025

  35. [35]

    In: Bengio, Y., LeCun, Y

    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings (2015),http://arxiv.org/abs/1409.1556

    Pith/arXiv arXiv 2015

  36. [36]

    In: Ran- zato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W

    Sitzmann, V., Rezchikov, S., Freeman, B., Tenenbaum, J., Durand, F.: Light field networks: Neural scene representations with single-evaluation rendering. In: Ran- zato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems. vol. 34, pp. 19313–19325. Curran Asso- ciates,Inc.(2021),https://procee...

    2021

  37. [37]

    In: Proceedings of the 32nd ACM International Conference on Multimedia

    Sun, X., Lee, J.C., Rho, D., Ko, J.H., Ali, U., Park, E.: F-3dgs: Factorized coor- dinates and representations for 3d gaussian splatting. In: Proceedings of the 32nd ACM International Conference on Multimedia. pp. 7957–7965 (2024)

    2024

  38. [38]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

    Tseng,Y.J.,Kao,C.H.,Chen,J.Z.,Gnutti,A.,Lo,S.Y.,Lin,Y.Y.,Peng,W.H.:Cs- gaussian: Progressive rate-distortion compression and segmentation for 3d gaussian splatting. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 6883–6892 (March 2026)

    2026

  39. [39]

    In: European Conference on Computer Vision

    Wang, H., Zhu, H., He, T., Feng, R., Deng, J., Bian, J., Chen, Z.: End-to-end rate-distortion optimized 3d gaussian representation. In: European Conference on Computer Vision. pp. 76–92. Springer (2024)

    2024

  40. [40]

    In: The Thirty- eighth Annual Conference on Neural Information Processing Systems (2024), https://openreview.net/forum?id=W2qGSMl2Uu

    Wang, Y., Li, Z., Guo, L., Yang, W., Kot, A., Wen, B.: ContextGS : Com- pact 3d gaussian splatting with anchor level context model. In: The Thirty- eighth Annual Conference on Neural Information Processing Systems (2024), https://openreview.net/forum?id=W2qGSMl2Uu

    2024

  41. [41]

    IEEE Transactions on Image Processing 13(4), 600–612 (Apr 2004)

    Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13(4), 600–612 (2004).https://doi.org/10.1109/TIP.2003.819861

    work page doi:10.1109/tip.2003.819861 2004

  42. [42]

    In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR) (2025)

    Zhan, Y.T., Ho, C.Y., Yang, H., Chen, Y.H., Chiang, J.C., Liu, Y.L., Peng, W.H.: CAT-3DGS: A context-adaptive triplane approach to rate-distortion-optimized 3DGS compression. In: Proceedings of the Thirteenth International Conference on Learning Representations (ICLR) (2025)

    2025

  43. [43]

    In: 2025 33rd European Signal Process- ing Conference (EUSIPCO)

    Zhan, Y.T., Yang, H.b., Ho, C.Y., Chiang, J.C., Peng, W.H.: Cat-3dgs pro: A new benchmark for efficient 3dgs compression. In: 2025 33rd European Signal Process- ing Conference (EUSIPCO). pp. 1367–1371 (2025).https://doi.org/10.23919/ EUSIPCO63237.2025.11226320

    arXiv 2025

  44. [44]

    In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018

    Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreason- able effectiveness of deep features as a perceptual metric. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 586–595 (2018). https://doi.org/10.1109/CVPR.2018.00068

    work page doi:10.1109/cvpr.2018.00068 2018

  45. [45]

    RLAIF-V: open-source AI feedback leads to super GPT-4V trustworthiness

    Zhang, Y., Jia, W., Niu, W., Yin, M.: Gaussianspa: An "optimizing-sparsifying" simplification framework for compact and high-quality 3d gaussian splatting. In: 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 26673–26682 (2025).https://doi.org/10.1109/CVPR52734.2025. 02484 18 H.-B. Yang et al

    work page doi:10.1109/cvpr52734.2025 2025

  46. [46]

    In: Forty-second International Conference on Machine Learning (2025),https://openreview.net/forum?id=ij0vj0BC72

    Zhou, H., Ni, Z.: Perceptual-GS: Scene-adaptive perceptual densification for gaus- sian splatting. In: Forty-second International Conference on Machine Learning (2025),https://openreview.net/forum?id=ij0vj0BC72

    2025