Pith. sign in

REVIEW 2 major objections 1 minor 55 references

From Sparse and Imperfect 2D Anchors to Consistent 3D Gaussian Street Scenes: Support-Aware Appearance

T0 review · 2 major / 1 minor · reviewed 2026-06-25 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Teacher-relative residual distillation bakes consistent appearance into 3D Gaussian street scenes from sparse imperfect 2D anchors.

desk verdict The paper gives a practical residual-distillation pipeline to bake consistent appearance into 3D Gaussians from sparse imperfect 2D anchors for street scenes, but the gains rest on an unverified assumption that residuals form a cleanly separable space. read the letter →

arxiv 2606.26007 v1 pith:WMTPYR5F submitted 2026-06-24 cs.CV cs.GR

Long Cao , Zhongquan Wang , Jie Li , Yuhan Chen , Kefei Qian , Xiangfei Huang , Guofa Li This is my paper · ORCID
classification cs.CVcs.GR
keywords 3DGaussianSplattingAppearanceBakingStreetSceneReconstructionSparseAnchorsResidualDistillationCross-viewConsistencySupport-awareOptimizationSphericalHarmonics
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper establishes that residuals between teacher anchors and original renders create a structured space supporting frequency decomposition, confidence estimation, and primitive-level lifting. This process admits supported detail while suppressing noise through confidence-gated optimization before baking residuals into fixed spherical-harmonic coefficients. A reader would care because independent view edits produce incoherent 3D targets and direct fitting spreads view-specific noise, whereas the new pipeline jointly manages imperfect sparse anchors for deployment with standard rasterizers. The teacher and auxiliary modules are removed after baking. Evaluations on Waymo assets and Tanks and Temples scenes demonstrate improved target alignment, content preservation, artifact suppression, and cross-view consistency relative to editing baselines.

What carries the argument

teacher-relative appearance residual distillation that uses residuals to enable frequency decomposition, confidence estimation, and support-aware primitive lifting

What would settle it

If the method produces visible new artifacts or lower cross-view consistency scores than direct fitting baselines when tested on the same Waymo street assets, the separation claim would be falsified.

Watch

Extended reading notes

Core claim

Teacher-relative appearance residual distillation forms a structured space from residuals between teacher anchors and original renders that enables frequency decomposition, confidence estimation, and primitive-level lifting. Renderer-space matching supplies the direct optimization signal and support-aware Gaussian-space aggregation regularizes assignment. Confidence-gated coarse-to-fine optimization admits supported detail and suppresses unsupported noise, after which all residuals bake into fixed-geometry spherical-harmonic coefficients. The teacher and auxiliary modules are discarded at inference.

Load-bearing premise

Residuals between teacher anchors and original renders form a structured space that permits reliable separation of supported detail from unsupported noise without introducing new artifacts.

Editorial extensions

If this is right

  • Baked 3D Gaussians deliver a favorable balance of target alignment, content preservation, artifact suppression, and cross-view consistency over editing-based baselines on Waymo and Tanks and Temples data.
  • The pipeline handles multiple target conditions while preserving the original geometry for standard rasterizer deployment.
  • Ablation results confirm the contribution of the main components including residual-space decomposition and confidence gating.
  • Only the fixed-geometry spherical-harmonic coefficients remain after the teacher and auxiliary modules are removed.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The residual-space structure may allow the same pipeline to incorporate additional target conditions such as weather or time-of-day changes without retraining the underlying geometry.
  • If the confidence estimation generalizes, the approach could reduce the number of required anchor views for new scenes while maintaining consistency.
  • The fixed-geometry output suggests direct compatibility with existing 3D Gaussian rendering engines used in simulation or robotics without custom inference code.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 1 minor

Summary. The paper introduces teacher-relative appearance residual distillation to bake consistent appearance into 3D Gaussian street scenes from sparse and imperfect 2D anchors. Residuals between teacher anchors and original renders are used to create a space for frequency decomposition, confidence estimation, and primitive-level lifting. The pipeline employs renderer-space matching for the optimization signal, support-aware Gaussian-space aggregation for regularization, and confidence-gated coarse-to-fine optimization before baking residuals into fixed spherical-harmonic coefficients. Teacher and auxiliary modules are discarded at inference. The central claim is that this yields a favorable balance of target alignment, content preservation, artifact suppression, and cross-view consistency over editing-based baselines on Waymo street assets and Tanks and Temples scenes, with ablations supporting the components.

Significance. If the residual space is sufficiently structured to enable reliable separation of supported detail from noise at the primitive level without introducing new artifacts, the approach would address a practical gap in deploying 3D Gaussian splatting for street scenes under imperfect anchors and standard rasterizers. The explicit discard of teacher modules at inference and the planned code release are positive for reproducibility.

major comments (2)
  1. [Abstract] Abstract: The evaluation claim of a 'favorable overall balance' over baselines is stated without any quantitative metrics, tables, error bars, or statistical comparisons. This directly undermines assessment of the central claim that the method outperforms editing baselines in alignment, preservation, suppression, and consistency.
  2. [Abstract] Abstract (pipeline description): The method assumes residuals between teacher anchors and renders form a structured space permitting frequency decomposition and confidence-gated lifting that separates detail from noise without new artifacts. No synthetic ground-truth validation, decomposition metrics, or failure-case analysis of this separation is described, which is load-bearing for the superiority claim.
minor comments (1)
  1. [Abstract] The abstract mentions 'multiple target conditions' and 'ablations confirm the effectiveness' but provides no details on the conditions, ablation setups, or quantitative outcomes of those ablations.

Simulated Author's Rebuttal

2 responses · 0 unresolved

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

read point-by-point responses

  1. Referee: [Abstract] Abstract: The evaluation claim of a 'favorable overall balance' over baselines is stated without any quantitative metrics, tables, error bars, or statistical comparisons. This directly undermines assessment of the central claim that the method outperforms editing baselines in alignment, preservation, suppression, and consistency.

    Authors: The abstract is a high-level summary; the manuscript provides full quantitative support in Sections 4.1-4.2 and Tables 1-3, including PSNR/SSIM/LPIPS values, error bars across runs, and direct comparisons to editing baselines. To strengthen the abstract's claim, we will revise it to briefly reference key quantitative improvements (e.g., average PSNR gains). revision: yes

  2. Referee: [Abstract] Abstract (pipeline description): The method assumes residuals between teacher anchors and renders form a structured space permitting frequency decomposition and confidence-gated lifting that separates detail from noise without new artifacts. No synthetic ground-truth validation, decomposition metrics, or failure-case analysis of this separation is described, which is load-bearing for the superiority claim.

    Authors: Our evaluation targets real-world imperfect anchors on Waymo and Tanks and Temples, where synthetic ground truth does not exist. Section 4.3 ablations and visual results quantify the benefit of frequency decomposition and confidence gating on the target metrics. We will add explicit failure-case analysis and decomposition visualizations in the revision and supplement to further substantiate the separation behavior. revision: partial

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; derivation relies on external teacher models and independent evaluations

full rationale

The paper presents a pipeline of teacher-relative residual distillation, renderer-space matching, support-aware aggregation, and confidence-gated optimization to bake appearance into 3D Gaussians. No equations or steps are shown reducing a claimed prediction or result to a fitted parameter or self-defined input by construction. The method explicitly discards teacher modules at inference and evaluates on external benchmarks (Waymo, Tanks and Temples) without load-bearing self-citations or renaming of known results as new derivations. The central assumption about residual structure is an empirical premise, not a definitional loop.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities can be extracted or verified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of From Sparse and Imperfect 2D Anchors to Consistent 3D Gaussian Street Scenes: Support-Aware Appearance." pith.science (2026). https://pith.science/paper/WMTPYR5F

@misc{pith2026260626007,
  author       = {Pith},
  title        = {Pith review of: From Sparse and Imperfect 2D Anchors to Consistent 3D Gaussian Street Scenes: Support-Aware Appearance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WMTPYR5F}},
  note         = {Machine review of arXiv:2606.26007}
}
read the original abstract

Image priors can synthesize target conditions for 3D Gaussian street scenes, but independently edited views do not define a coherent 3D target. Direct fitting can propagate view-specific noise, while existing pipelines do not jointly handle imperfect sparse anchors and standard-rasterizer deployment. To address this gap, teacher-relative appearance residual distillation is introduced for appearance baking. A structured space for frequency decomposition, confidence estimation, and primitive-level lifting is formed by residuals between teacher anchors and original renders. The direct optimization signal is supplied by renderer-space matching, while primitive assignment is regularized by support-aware Gaussian-space aggregation. Supported detail is admitted and unsupported noise is suppressed through confidence-gated coarse-to-fine optimization, after which all residuals are baked into fixed-geometry spherical-harmonic coefficients. The teacher and auxiliary training modules are discarded at inference. Evaluation across Waymo street assets, Tanks and Temples scenes, and multiple target conditions shows a favorable overall balance of target alignment, content preservation, artifact suppression, and cross-view consistency over editing-based baselines. Ablations confirm the effectiveness of the main components. Code will be released at https://github.com/Cagares/Baking-for-3D-Gaussian.

Figures

Figures reproduced from arXiv: 2606.26007 by the authors.

Figure 1
Figure 1. Overview of the framework. (a) Sparse teacher anchors define the target condition. (b) Teacher-relative residuals are distilled with support-aware [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Target alignment versus cross-view error with 95% scene-cluster [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Qualitative comparison with matched cameras. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Five consecutive views of a Waymo scene under sunset. [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Sparse-anchor visual comparison with 4 or 8 teacher views. [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Residual-domain ablation on a Waymo sunset case using a Qwen image-edit teacher. [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

55 extracted references · 1 canonical work pages

  1. [1]

    3D gaussian splatting for real-time radiance field rendering,

    B. Kerbl, G. Kopanas, T. Leimkuehler, and G. Drettakis, “3D gaussian splatting for real-time radiance field rendering,”ACM Transactions on Graphics, vol. 42, no. 4, 2023

    2023

  2. [2]

    Relighting from a single image: Datasets and deep intrinsic-based architecture,

    Y . Yang, H. A. Sial, R. Baldrich, and M. Vanrell, “Relighting from a single image: Datasets and deep intrinsic-based architecture,”IEEE Transactions on Multimedia, 2025, accepted for publication

    2025

  3. [3]

    Neural gaffer: Relighting any object via diffusion,

    H. Jin, Y . Li, F. Luan, Y . Xiangli, S. Bi, K. Zhang, Z. Xu, J. Sun, and N. Snavely, “Neural gaffer: Relighting any object via diffusion,” in Advances in Neural Information Processing Systems (NeurIPS), 2024

    2024

  4. [4]

    DiLightNet: Fine-grained lighting control for diffusion-based image generation,

    C. Zeng, Y . Dong, P. Peers, Y . Kong, H. Wu, and X. Tong, “DiLightNet: Fine-grained lighting control for diffusion-based image generation,” in ACM SIGGRAPH Conference Papers, 2024

    2024

  5. [5]

    SwitchLight: Co-design of physics-driven and data-driven AI for professional portrait lighting,

    H. Kim, M. Lee, J. Park, H. Kim, and S. Lee, “SwitchLight: Co-design of physics-driven and data-driven AI for professional portrait lighting,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  6. [6]

    DiffusionLight: Light probes for free by painting a chrome ball,

    P. Phongthawee, W. Chinchuthakun, N. Sinsunthithet, and S. Suwa- janakorn, “DiffusionLight: Light probes for free by painting a chrome ball,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  7. [7]

    AnlightenDiff: An- choring diffusion probabilistic model on low-light image enhancement,

    C.-Y . Chan, W.-C. Siu, Y .-H. Chan, and H. A. Chan, “AnlightenDiff: An- choring diffusion probabilistic model on low-light image enhancement,” IEEE Transactions on Image Processing, vol. 33, pp. 6324–6339, 2024

    2024

  8. [8]

    LumiNet: Latent intrinsics meets diffusion models for indoor scene relighting,

    T. Xing, L. Xu, J. Guo, Q. Zhang, Y . Xiong, W. Chen, X. Li, and L. Wang, “LumiNet: Latent intrinsics meets diffusion models for indoor scene relighting,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 1254– 1264

    2025

Show all 55 references
  1. [9]

    Scribble- Light: Single image indoor relighting with scribbles,

    G. Choi, W. Lee, S. Lee, J. Lee, W. Jin, S. Lee, and H. Jung, “Scribble- Light: Single image indoor relighting with scribbles,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 12114–12124

    2025

  2. [10]

    LightSwitch: Multi-view relighting with material-guided diffusion,

    Y . Litman, O. Patashnik, K. Deng, A. Bansal, O. Gafni, D. Parikh, D. Lischinski, and D. Cohen-Or, “LightSwitch: Multi-view relighting with material-guided diffusion,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 622– 632

    2025

  3. [11]

    A comprehensive frame- work for human relighting with correspondence learning and temporal shading refinement,

    C. Wang, H. Zhu, Z. Fu, Z. Che, and S. Gu, “A comprehensive frame- work for human relighting with correspondence learning and temporal shading refinement,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 15915– 15925

    2025

  4. [12]

    TensoIR: Tensorial inverse rendering,

    H. Jin, I. Liu, P. Xu, X. Zhang, S. Han, S. Bi, X. Zhou, Z. Xu, and H. Su, “TensoIR: Tensorial inverse rendering,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 165–174

    2023

  5. [13]

    NeRO: Neural geometry and BRDF reconstruction of reflective objects from multiview images,

    Y . Liu, P. Wang, C. Lin, X. Long, J. Wang, L. Liu, T. Komura, and W. Wang, “NeRO: Neural geometry and BRDF reconstruction of reflective objects from multiview images,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023

    2023

  6. [14]

    NeILF++: Inter-reflectable light fields for geometry and material estimation,

    K. Zhang, F. Luan, Q. Wang, K. Bala, and N. Snavely, “NeILF++: Inter-reflectable light fields for geometry and material estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

    2023

  7. [15]

    GIR: 3D gaussian inverse rendering for relightable scene factorization,

    Y . Shi, Y . Wu, C. Wu, X. Liu, C. Zhao, H. Feng, J. Zhang, B. Zhou, E. Ding, and J. Wang, “GIR: 3D gaussian inverse rendering for relightable scene factorization,”arXiv preprint arXiv:2312.05133, 2023

    2023

  8. [16]

    GS-IR: 3D gaus- sian splatting for inverse rendering,

    Z. Liang, Q. Zhang, Y . Feng, Y . Shan, and K. Jia, “GS-IR: 3D gaus- sian splatting for inverse rendering,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  9. [17]

    GaussianShader: 3D gaussian splatting with shading functions for reflective surfaces,

    Y . Jiang, J. Zhang, D. Xu, Y . Wei, and Q. Chen, “GaussianShader: 3D gaussian splatting with shading functions for reflective surfaces,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  10. [18]

    GS3: Efficient relighting with triple gaussian splatting,

    Z. Bi, Y . Zeng, C. Zeng, F. Pei, X. Feng, K. Zhou, and H. Wu, “GS3: Efficient relighting with triple gaussian splatting,” inACM SIGGRAPH Asia Conference Papers, 2024

    2024

  11. [19]

    H. Chen, Z. Lin, and J. Zhang, “GI-GS: Global illumination decom- position on gaussian splatting for inverse rendering,’ inInternational Conference on Learning Representations (ICLR), 2025

    2025

  12. [20]

    H. Sun, Y . Gao, J. Xie, J. Yang, and B. Wang, “SVG-IR: Spatially- varying gaussian splatting for inverse rendering,’ inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025

    2025

  13. [21]

    K. Ye, C. Gao, G. Li, W. Chen, and B. Chen, “GeoSplatting: Towards ge- ometry guided gaussian splatting for physically-based inverse rendering,’ inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025. 10

    2025

  14. [22]

    RTR-GS: 3D gaussian splatting for inverse rendering with radiance transfer and reflection,

    Y . Zhou, F. Zhang, Z. Wang, and L. Zhang, “RTR-GS: 3D gaussian splatting for inverse rendering with radiance transfer and reflection,” in Proceedings of the ACM International Conference on Multimedia, 2025

    2025

  15. [23]

    Liang, H

    Z. Liang, H. Li, K. Jia, K. Guo, and Q. Zhang, “GUS-IR: Gaussian splatting with unified shading for inverse rendering,’IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  16. [24]

    GlossyGS: Inverse rendering of glossy objects with 3D gaussian splatting,

    S. Lai, L. Huang, J. Guo, K. Cheng, B. Pan, X. Long, J. Lyu, C. Lv, and Y . Guo, “GlossyGS: Inverse rendering of glossy objects with 3D gaussian splatting,”IEEE Transactions on Visualization and Computer Graphics, 2025

    2025

  17. [25]

    2D gaussian splatting for outdoor scene decomposition and relighting,

    W. Feng, K. Ye, Q. Zhang, Q. Zhang, and N. Li, “2D gaussian splatting for outdoor scene decomposition and relighting,” inProceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2025

    2025

  18. [26]

    RNG: Relightable neural gaussians,

    R. Fan, A. S, and S. Soatto, “RNG: Relightable neural gaussians,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 2882–2891

    2025

  19. [27]

    ReCap: Better gaussian relighting with cross-environment captures,

    H. Li, A. Bufe, M. Hanika, B. Wang, and H. P. A. Lensch, “ReCap: Better gaussian relighting with cross-environment captures,” inProceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 15729–15739

    2025

  20. [28]

    GS-ID: Illumination decompo- sition on gaussian splatting via adaptive light aggregation and diffusion- guided material priors,

    K. Du, Z. Liang, Y . Shen, and Z. Wang, “GS-ID: Illumination decompo- sition on gaussian splatting via adaptive light aggregation and diffusion- guided material priors,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 26220–26229

    2025

  21. [29]

    GaRe: Relightable 3D gaussian splatting for outdoor scenes from unconstrained photo collections,

    Z. Bai, T. Yang, H. Bao, G. Zhang, and Z. Cui, “GaRe: Relightable 3D gaussian splatting for outdoor scenes from unconstrained photo collections,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025

    2025

  22. [30]

    SU-RGS: Relightable 3D gaussian splatting from sparse views under unconstrained illumination,

    W. Zhang, G. Qian, C. Lyu, J. Sun, C. Zhang, X. Lin, L. Zhang, and X. Qi, “SU-RGS: Relightable 3D gaussian splatting from sparse views under unconstrained illumination,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025

    2025

  23. [31]

    Mip-splatting: Alias-free 3D gaussian splatting,

    Z. Yu, A. Chen, B. Huang, T. Sattler, and A. Geiger, “Mip-splatting: Alias-free 3D gaussian splatting,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19447–19456

    2024

  24. [32]

    Scaffold-GS: Structured 3D gaussians for view-adaptive rendering,

    T. Lu, M. Yu, L. Xu, Y . Xiangli, L. Wang, D. Lin, and B. Dai, “Scaffold-GS: Structured 3D gaussians for view-adaptive rendering,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  25. [33]

    2D gaussian splatting for geometrically accurate radiance fields,

    B. Huang, Z. Yu, A. Chen, A. Geiger, and S. Gao, “2D gaussian splatting for geometrically accurate radiance fields,” inACM SIGGRAPH Conference Papers, 2024

    2024

  26. [34]

    SuGaR: Surface-aligned gaussian splatting for efficient 3D mesh reconstruction and high-quality mesh rendering,

    A. Guedon and V . Lepetit, “SuGaR: Surface-aligned gaussian splatting for efficient 3D mesh reconstruction and high-quality mesh rendering,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 5354–5363

    2024

  27. [35]

    VastGaussian: Vast 3D gaussians for large scene reconstruction,

    J. Lin, Z. Li, X. Tang, J. Liu, S. Liu, J. Liu, Y . Lu, X. Wu, S. Xu, Y . Yan, and W. Yang, “VastGaussian: Vast 3D gaussians for large scene reconstruction,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  28. [36]

    CityGaussian: Real-time high-quality large-scale scene rendering with gaussians,

    Y . Liu, C. Luo, L. Fan, N. Wang, J. Peng, and Z. Zhang, “CityGaussian: Real-time high-quality large-scale scene rendering with gaussians,” in Proceedings of the European Conference on Computer Vision (ECCV), 2024

    2024

  29. [37]

    Street gaussians for modeling dynamic urban scenes,

    Y . Yan, H. Lin, C. Zhou, W. Wang, H. Sun, K. Zhan, X. Lang, X. Zhou, and S. Peng, “Street gaussians for modeling dynamic urban scenes,” in Proceedings of the European Conference on Computer Vision (ECCV), 2024

    2024

  30. [38]

    Driv- ingGaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes,

    X. Zhou, Z. Lin, X. Shan, Y . Wang, D. Sun, and M.-H. Yang, “Driv- ingGaussian: Composite gaussian splatting for surrounding dynamic au- tonomous driving scenes,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

    2024

  31. [39]

    A decoupled multi- task network for shadow removal,

    J. Liu, Q. Wang, H. Fan, W. Li, L. Qu, and Y . Tang, “A decoupled multi- task network for shadow removal,”IEEE Transactions on Multimedia, vol. 25, pp. 9449–9463, 2023

    2023

  32. [40]

    Retinex-based variational framework for low-light image enhancement and denoising,

    Q. Ma, Y . Wang, and T. Zeng, “Retinex-based variational framework for low-light image enhancement and denoising,”IEEE Transactions on Multimedia, vol. 25, pp. 5580–5588, 2023

    2023

  33. [41]

    D. Ye, Z. Ni, W. Yang, H. Wang, S. Wang, and S. Kwong, “Glow in the dark: Low-light image enhancement with external memory,’IEEE Transactions on Multimedia, vol. 26, pp. 2148–2163, 2024

    2024

  34. [42]

    X. Wang, K. Chen, Z. Wang, and W. Huang, “PMSNet: Parallel multi- scale network for accurate low-light light-field image enhancement,’ IEEE Transactions on Multimedia, vol. 26, pp. 2041–2055, 2024

  35. [43]

    Unsu- pervised low-light image enhancement with self-paced learning,

    Y . Luo, X. Chen, J. Ling, C. Huang, W. Zhou, and G. Yue, “Unsu- pervised low-light image enhancement with self-paced learning,”IEEE Transactions on Multimedia, vol. 27, pp. 1808–1820, 2025

    2025

  36. [44]

    Om- niTester: Multimodal large language model driven scenario testing for autonomous vehicles,

    Q. Lu, X. Wang, Y . Jiang, G. Zhao, M. Ma, and S. Feng, “Om- niTester: Multimodal large language model driven scenario testing for autonomous vehicles,”Automotive Innovation, vol. 8, pp. 838–852, 2025

    2025

  37. [45]

    Towards scalable multi-view reconstruction of geometry and materials,

    C. Schmitt, B. Antic, A. Neculai, J. H. Lee, and A. Geiger, “Towards scalable multi-view reconstruction of geometry and materials,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, pp. 15850–15869, 2023

    2023

  38. [46]

    InvGS: A novel real-time inverse rendering frame- work utilizing 3D gaussian splatting,

    Y . Pu and Q. Wu, “InvGS: A novel real-time inverse rendering frame- work utilizing 3D gaussian splatting,” inProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025

    2025

  39. [47]

    StyleGaussian: Instant 3D style transfer with gaussian splatting,

    K. Liu, F. Zhan, M. Xu, C. Theobalt, L. Shao, and S. Lu, “StyleGaussian: Instant 3D style transfer with gaussian splatting,” inSIGGRAPH Asia 2024 Technical Communications. New York, NY , USA: ACM, 2024, pp. 21:1–21:4

    2024

  40. [48]

    Fast multi-view consistent 3D editing with video priors,

    L. Chen, R. Li, G. Zhang, P. Wang, and L. Zhang, “Fast multi-view consistent 3D editing with video priors,” inProceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2026

    2026

  41. [49]

    EditSplat: Multi-view fusion and attention-guided optimization for view-consistent 3D scene editing with 3D gaussian splatting,

    D. I. Lee, H. Park, H. Park, J. Seo, E. Park, H. D. Baek, S. Shin, S. Kim, and S. Kim, “EditSplat: Multi-view fusion and attention-guided optimization for view-consistent 3D scene editing with 3D gaussian splatting,” inProceedings of the IEEE/CVF Conference on Computer Vision ...

    2025

  42. [50]

    Image generation: OpenAI API,

    OpenAI, “Image generation: OpenAI API,” 2026. [Online]. Available: https://platform.openai.com/docs/guides/image-generation

    2026

  43. [51]

    Reproducible scaling laws for contrastive language-image learning,

    M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev, “Reproducible scaling laws for contrastive language-image learning,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 20...

    2023

  44. [52]

    Adam: A method for stochastic optimization,

    D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” inInternational Conference on Learning Representations (ICLR), 2015

    2015

  45. [53]

    Learning transferable visual models from natural lan- guage supervision,

    A. Radford et al., “Learning transferable visual models from natural lan- guage supervision,” inInternational Conference on Machine Learning (ICML), 2021, pp. 8748–8763

    2021

  46. [54]

    Scalability in perception for autonomous driving: Waymo open dataset,

    P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V . Patnaik, P. Tsui, J. Guo, Y . Zhou, Y . Chai, B. Caine, V . Vasudevan, W. Han, J. Ngiam, H. Zhao, A. Timofeev, S. Ettinger, M. Krivokon, A. Gao, A. Joshi, Y . Zhang, J. Shlens, Z. Chen, and D. Anguelov, “Scalability in perc...

    2020

  47. [55]

    Tanks and temples: Benchmarking large-scale scene reconstruction,

    A. Knapitsch, J. Park, Q.-Y . Zhou, and V . Koltun, “Tanks and temples: Benchmarking large-scale scene reconstruction,”ACM Transactions on Graphics, vol. 36, no. 4, Art. no. 78, 2017

    2017

Pith tools

Reviewed June 25, 2026 · model on record in the stance chip above.