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REVIEW 3 major objections 5 minor 1 cited by

Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This survey argues that 3D Gaussian Splatting is a natural fit for extended reality, yet only three systems have actually built for XR, and it maps where the field should go next.

desk verdict Useful XR-focused 3DGS roadmap with a defensible taxonomy, but the 152/272 count is unverifiable and should be softened or backed by released screening data. read the letter →

arxiv 2412.06257 v2 pith:MIZSGAOI submitted 2024-12-09 cs.CV cs.GRcs.HC

Shi Qiu , Binzhu Xie , Qixuan Liu , Pheng-Ann Heng This is my paper · ORCID
classification cs.CVcs.GRcs.HC
keywords 3DGaussianSplattingExtendedRealityVirtualAugmentedNeuralRenderingtaxonomyhumanavatar
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

This paper reviews 272 published 3D Gaussian Splatting (3DGS) papers and finds that while 152 mention extended reality (XR), only three systems, VR-GS, DualGS, and RGCA, are explicitly built for XR environments. It argues that the direct application of 3DGS inside XR is underexplored, despite the technology's real-time performance and explicit 3D representation making it a natural fit for immersive systems. To organize the opportunity, the paper proposes a taxonomy of five innovation areas, 3D content creation, rendering and visualization, interaction and manipulation, system optimization and efficiency, and specialized applications, and lists six future research directions. A sympathetic reader would take this as a roadmap: 3DGS is ready, but the XR community has barely started to use it.

What carries the argument

The load-bearing object is the taxonomy itself, constructed by keyword-screening a corpus of 272 publicly available 3DGS papers and classifying their innovations into five XR-relevant areas: 3D content creation, rendering and visualization, interaction and manipulation, system optimization and efficiency, and specialized applications (including SLAM and medical XR). The taxonomy is what turns a collection of loosely related rendering papers into an argument that XR is a distinct, tractable application ground. Its companion device is the identification of the three direct XR systems, VR-GS, DualGS, and RGCA, each of which supplies a concrete technical template: physics-aware deformation via XPBD, joint/skin Gaussian decoupling plus compression, and relightable appearance with explicit eye and hair models.

What would settle it

A reproducible literature search with declared databases, queries, and inclusion criteria that finds more than three 3DGS systems with explicit XR implementations and evaluations would falsify the underexploration claim. For instance, a forward search for 3DGS papers mentioning 'headset' or 'passthrough' and reporting end-to-end XR deployment would settle the count.

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Extended reading notes

Core claim

The paper's central claim is that the intersection of 3DGS and XR is not merely nascent but underexplored: although a majority of 3DGS papers reference XR concepts, almost none demonstrate 3DGS functioning inside an XR system. The authors identify exactly three exceptions, VR-GS (physics-aware interactive editing in VR), DualGS (compressed volumetric human performance for XR), and RGCA (relightable codec avatars with gaze control), and treat these as existence proofs for what a dedicated 3DGS-XR system looks like. From a screened corpus of 272 papers, the authors build a five-category taxonomy of 3DGS innovations they consider relevant to XR, and they propose mesh modeling, dynamic scene representation, open-world scene understanding, hand tracking, passthrough capabilities, and immersive visualization as the most promising forward directions.

Load-bearing premise

The survey's count of 'only three' direct XR systems rests on the authors' unpublished screening of 272 papers; if the search queries or inclusion criteria change, the number of XR-specific systems could change materially.

Editorial extensions

If this is right

  • 3DGS research that targets XR should focus on system-level integration rather than generic rendering, since the paper's screening shows that is the unoccupied space.
  • The three direct XR systems, VR-GS, DualGS, and RGCA, provide concrete templates for physics-aware editing, compressed volumetric video, and relightable avatars that future XR work can build on.
  • The proposed future directions, such as passthrough reconstruction, open-world scene understanding, hand tracking, and immersive data visualization, define concrete areas where 3DGS innovation is most likely to pay off for XR.
  • The taxonomy implies that progress in XR will depend on advances across all five innovation areas together, not on rendering alone.

Reading between the lines

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

  • A direct extension of the paper's method would be to publish the screening protocol and corpus so the 'only three' count can be independently verified and updated.
  • If the underexploration claim holds, then a standardized benchmark measuring latency, render quality, and interaction responsiveness of 3DGS inside headset pipelines would be the logical next step, though the paper does not propose one.
  • The three demonstrated systems all center on human bodies or heads, suggesting that telepresence and avatar applications will drive early 3DGS adoption in XR faster than other domains.
  • The taxonomy's separation of interaction and manipulation from rendering points to a deeper, implicit claim: XR needs controllable geometry and physics, not just photorealism, which is why explicit 3DGS may fit XR better than implicit neural fields.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper is a survey/roadmap at the intersection of 3D Gaussian Splatting (3DGS) and Extended Reality (XR). The authors state that they reviewed 272 publicly available 3DGS papers, that 152 explicitly mention XR-related concepts, and that only three works (VR-GS, DualGS, RGCA) specifically target XR. They propose a five-part taxonomy of 3DGS innovations relevant to XR: 3D Content Creation, Rendering & Visualization, Interaction & Manipulation, System Optimization & Efficiency, and Specialized Applications. They then discuss future research directions including mesh modeling, dynamic scene representation, open-world scene understanding, hand tracking, passthrough capabilities, and immersive visualization. The central claim is that direct application of 3DGS in XR is notably underexplored, despite the technology's promise.

Significance. The paper addresses a timely and useful topic: no prior survey focuses specifically on the 3DGS-XR intersection, and the authors provide a reasonable qualitative organization of relevant innovations. The proposed taxonomy and future-directions list could serve as a starting point for researchers entering this area. The paper also honestly acknowledges in Section V that some works may have been omitted. However, the quantitative claims about the corpus (272 papers, 152 XR-mentioning, three specifically targeting XR) are not reproducible from the manuscript as written, and the classification decisions are not documented. Because the survey's novelty and roadmap are motivated by the claimed gap, this methodology gap is load-bearing. The paper would be significantly strengthened by a transparent screening protocol, a released paper list, and explicit criteria for the 'specifically targeting XR' category.

major comments (3)
  1. [Section I, third paragraph] The claim that the survey is based on '272 publicly available papers' and that '152 out of 272' explicitly mention XR concepts is not accompanied by any description of the search queries, databases, time window, inclusion/exclusion criteria, or paper list. Since the paper's central thesis, that XR applications of 3DGS are underexplored, depends directly on these counts and on how XR relevance was judged, the methodology must be reported or the quantitative claims must be softened. Please provide a reproducible protocol (e.g., an appendix with the corpus, keyword list, screening procedure, and ideally inter-annotator agreement) or reframe the argument as a qualitative observation.
  2. [Section II-A] The statement 'we have identified three representative works that specifically leverage 3DGS to advance XR research and development' lacks an operational definition of 'representative' and 'specifically target XR'. It is therefore not checkable whether other works that discuss AR/VR demonstrations or XR-related hardware, such as those cited elsewhere in the taxonomy, were considered and excluded. Please define the inclusion criteria for this category and provide evidence that the selection is not arbitrary; alternatively, present the three works as illustrative examples rather than as an exhaustive enumeration.
  3. [Figure 2 and Section III] The taxonomy assigns individual papers to XR-relevant categories without demonstrating that each cited paper either mentions XR or evaluates its method in an XR setting. For example, reference [24] (StopThePop) is listed under 'Low-Latency Streaming' and 'High-Performance Rendering', but the paper is a general sorting optimization for Gaussian splatting and does not appear to include XR-specific evaluation. If some entries are included because the authors believe they have 'potential' for XR, that should be stated explicitly and distinguished from work that actually demonstrates XR use. Without a consistent mapping rule, the taxonomy's validity and the 'underexplored' conclusion are difficult to assess.
minor comments (5)
  1. [Section I, Figure 1] The word cloud is said to be 'extracted from recent 3DGS literature that references XR-related keywords', but no details are given about the keyword list, text sources, stop-word removal, or frequency normalization. Please describe the generation process or remove the figure.
  2. [Section II-A, DualGS paragraph] There is a typo: 'V olumetric videos' should be 'Volumetric videos'.
  3. [Section IV, Mesh Modeling] The example 'GS-VTON [95] enables VR content personalization based on user preferences' appears to mischaracterize the paper, which is a virtual try-on method; it is not clear that it targets VR. Additionally, references [93] and [94] are point cloud completion/surface reconstruction works, not 3DGS papers, and citing them in support of 'mesh modeling' needs clarification.
  4. [Section V] The statement 'we may have omitted some works' in the conclusion is appropriate but sits in tension with the earlier claim of a 'comprehensive review'. Please state the limitations of the search procedure earlier in the paper, e.g., in the introduction or a dedicated methodology paragraph.
  5. [References] The reference list has inconsistent formatting: some entries lack venue details (e.g., [10], [51], [55]), some use 'arXiv preprint' without an identifier, and the author lists contain occasional spacing issues (e.g., 'Y' instead of 'Y.' in several entries). A careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's conclusions are empirical literature assessments, and the unreported screening protocol is a reproducibility issue rather than a circular derivation.

full rationale

This paper is a survey, not a derivation: there is no fitted parameter renamed as a prediction, no quantity computed from an input that already contains the result, and no uniqueness claim imported from the authors' prior work. The central claim that direct 3DGS application in XR remains underexplored rests on a manual screening of 272 papers (152 explicitly mention XR) and on the identification of three representative XR-targeting works in Section II-A. That classification is unreported and not independently checkable, but that is a reproducibility and falsifiability weakness, not circularity: the conclusion is an empirical summary of the collected corpus rather than a logical consequence of the screening definition. The taxonomy in Figure 2 organizes papers by relevance to XR, and the same relevance criterion informs selection, yet no quantity is fitted and no predictive claim is derived from the taxonomy. The self-citations [93] and [94] appear only as background pointers in the future-prospects discussion of mesh modeling and are not load-bearing for the paper's thesis. No circular step can therefore be exhibited, and the honest finding is no significant circularity.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The central claims rest on the representativeness of the literature sample and on the suitability of 3DGS for XR. Neither is established with an external benchmark. No free parameters or invented entities are used.

assumptions (3)
  • domain assumption The 272 collected papers and the keyword screening are representative of the 3DGS-for-XR literature.
    Section I states the corpus spans from July 2023 to late October 2024 in top-tier venues, but no search protocol is given, so this is an unverified assumption that underlies all quantitative claims.
  • domain assumption 3D Gaussian Splatting is an appropriate core representation for real-time XR systems.
    The paper repeatedly states this premise, for example in the Introduction, but does not compare against alternatives like NeRF or mesh-based rendering under XR constraints.
  • ad hoc to paper The five proposed taxonomy areas are the relevant axes for XR applications.
    The taxonomy is constructed by the authors from their survey and is not derived from a formal analysis or external standard.

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Cite this review

Pith. "Pith review of Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects." pith.science (2026). https://pith.science/paper/MIZSGAOI

@misc{pith2026241206257,
  author       = {Pith},
  title        = {Pith review of: Advancing Extended Reality with 3D Gaussian Splatting: Innovations and Prospects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MIZSGAOI}},
  note         = {Machine review of arXiv:2412.06257}
}
read the original abstract

3D Gaussian Splatting (3DGS) has attracted significant attention for its potential to revolutionize 3D representation, rendering, and interaction. Despite the rapid growth of 3DGS research, its direct application to Extended Reality (XR) remains underexplored. Although many studies recognize the potential of 3DGS for XR, few have explicitly focused on or demonstrated its effectiveness within XR environments. In this paper, we aim to synthesize innovations in 3DGS that show specific potential for advancing XR research and development. We conduct a comprehensive review of publicly available 3DGS papers, with a focus on those referencing XR-related concepts. Additionally, we perform an in-depth analysis of innovations explicitly relevant to XR and propose a taxonomy to highlight their significance. Building on these insights, we propose several prospective XR research areas where 3DGS can make promising contributions, yet remain rarely touched. By investigating the intersection of 3DGS and XR, this paper provides a roadmap to push the boundaries of XR using cutting-edge 3DGS techniques.

Figures

Figures reproduced from arXiv: 2412.06257 by the authors.

Figure 1
Figure 1. Word cloud extracted from recent 3DGS literature [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Taxonomy of 3DGS Innovations Relevant to XR. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

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

  1. MS2Mesh-XR: Multi-modal Sketch-to-Mesh Generation in XR Environments

    cs.CV 2024-12 conditional novelty 3.0 of 10

    A system that turns mid-air sketches plus voice into textured 3D meshes in XR by chaining ControlNet image generation with convolutional mesh reconstruction.

Reference graph

Works this paper leans on

95 extracted references · 28 canonical work pages · cited by 1 Pith paper

  1. [24]

    Stopthepop: Sorted gaussian splatting for view-consistent real- time rendering,

    L. Radl, M. Steiner, M. Parger, A. Weinrauch, B. Kerbl, and M. Stein- berger, “Stopthepop: Sorted gaussian splatting for view-consistent real- time rendering,” ACM Transactions on Graphics (TOG) , vol. 43, no. 4, pp. 1–17, 2024

    work page 2024

  2. [1]

    Nerf: Representing scenes as neural radiance fields for view synthesis,

    B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021

    2021

  3. [2]

    D- nerf: Neural radiance fields for dynamic scenes,

    A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D- nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 318–10 327

    2021

  4. [3]

    Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,

    J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 5855–5864

    2021

  5. [4]

    Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction,

    P. Wang, L. Liu, Y . Liu, C. Theobalt, T. Komura, and W. Wang, “Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction,” arXiv preprint arXiv:2106.10689 , 2021

    arXiv 2021

  6. [5]

    3d gaussian splatting for real-time radiance field rendering

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

    2023

  7. [6]

    Human gaussian splatting: Real-time rendering of animatable avatars,

    A. Moreau, J. Song, H. Dhamo, R. Shaw, Y . Zhou, and E. P´erez-Pellitero, “Human gaussian splatting: Real-time rendering of animatable avatars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 788–798

    2024

  8. [7]

    Ash: Animatable gaussian splats for efficient and photoreal human rendering,

    H. Pang, H. Zhu, A. Kortylewski, C. Theobalt, and M. Habermann, “Ash: Animatable gaussian splats for efficient and photoreal human rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 1165–1175

    2024

Show all 95 references
  1. [8]

    Gavatar: Animatable 3d gaussian avatars with implicit mesh learning,

    Y . Yuan, X. Li, Y . Huang, S. De Mello, K. Nagano, J. Kautz, and U. Iqbal, “Gavatar: Animatable 3d gaussian avatars with implicit mesh learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 896–905

    2024

  2. [9]

    Gaussianavatar: Towards realistic human avatar modeling from a single video via animatable 3d gaussians,

    L. Hu, H. Zhang, Y . Zhang, B. Zhou, B. Liu, S. Zhang, and L. Nie, “Gaussianavatar: Towards realistic human avatar modeling from a single video via animatable 3d gaussians,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

    2024

  3. [10]

    Headgas: Real-time animatable head avatars via 3d gaussian splatting,

    H. Dhamo, Y . Nie, A. Moreau, J. Song, R. Shaw, Y . Zhou, and E. P ´erez- Pellitero, “Headgas: Real-time animatable head avatars via 3d gaussian splatting,” arXiv preprint arXiv:2312.02902 , 2023

    2023 arXiv

  4. [11]

    Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting,

    Z. Yang, H. Yang, Z. Pan, and L. Zhang, “Real-time photorealistic dynamic scene representation and rendering with 4d gaussian splatting,” arXiv preprint arXiv:2310.10642 , 2023

    2023 arXiv

  5. [12]

    Robust dual gaussian splatting for immersive human-centric volumetric videos,

    Y . Jiang, Z. Shen, Y . Hong, C. Guo, Y . Wu, Y . Zhang, J. Yu, and L. Xu, “Robust dual gaussian splatting for immersive human-centric volumetric videos,” arXiv preprint arXiv:2409.08353 , 2024

    2024 arXiv

  6. [13]

    Vr-gs: a physical dynamics-aware interactive gaussian splatting system in virtual reality,

    Y . Jiang, C. Yu, T. Xie, X. Li, Y . Feng, H. Wang, M. Li, H. Lau, F. Gao, Y . Yanget al. , “Vr-gs: a physical dynamics-aware interactive gaussian splatting system in virtual reality,” in ACM SIGGRAPH 2024 Conference Papers, 2024, pp. 1–1

    2024

  7. [14]

    Relightable gaussian codec avatars,

    S. Saito, G. Schwartz, T. Simon, J. Li, and G. Nam, “Relightable gaussian codec avatars,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 130–141

    2024

  8. [15]

    Gomavatar: Efficient animatable human modeling from monocular video using gaussians-on-mesh,

    J. Wen, X. Zhao, Z. Ren, A. G. Schwing, and S. Wang, “Gomavatar: Efficient animatable human modeling from monocular video using gaussians-on-mesh,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 2059–2069

    2024

  9. [16]

    Generalizable and animatable gaussian head avatar,

    X. Chu and T. Harada, “Generalizable and animatable gaussian head avatar,” arXiv preprint arXiv:2410.07971 , 2024

    2024 arXiv

  10. [17]

    Human101: Training 100+ fps human gaussians in 100s from 1 view,

    M. Li, J. Tao, Z. Yang, and Y . Yang, “Human101: Training 100+ fps human gaussians in 100s from 1 view,” arXiv preprint arXiv:2312.15258 , 2023

    2023 arXiv

  11. [18]

    3dgs- avatar: Animatable avatars via deformable 3d gaussian splatting,

    Z. Qian, S. Wang, M. Mihajlovic, A. Geiger, and S. Tang, “3dgs- avatar: Animatable avatars via deformable 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5020–5030

    2024

  12. [19]

    Flashavatar: High-fidelity head avatar with efficient gaussian embedding,

    J. Xiang, X. Gao, Y . Guo, and J. Zhang, “Flashavatar: High-fidelity head avatar with efficient gaussian embedding,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 1802–1812

    2024

  13. [20]

    Monogaussianavatar: Monocular gaussian point-based head avatar,

    Y . Chen, L. Wang, Q. Li, H. Xiao, S. Zhang, H. Yao, and Y . Liu, “Monogaussianavatar: Monocular gaussian point-based head avatar,” in ACM SIGGRAPH 2024 Conference Papers , 2024, pp. 1–9

    2024

  14. [21]

    Liv-gaussmap: Lidar-inertial-visual fusion for real-time 3d radiance field map rendering,

    S. Hong, J. He, X. Zheng, C. Zheng, and S. Shen, “Liv-gaussmap: Lidar-inertial-visual fusion for real-time 3d radiance field map rendering,” IEEE Robotics and Automation Letters , 2024

    2024

  15. [22]

    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 et al. , “Vastgaussian: Vast 3d gaussians for large scene reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 5166–5175

    2024

  16. [23]

    360-gs: Layout- guided panoramic gaussian splatting for indoor roaming,

    J. Bai, L. Huang, J. Guo, W. Gong, Y . Li, and Y . Guo, “360-gs: Layout- guided panoramic gaussian splatting for indoor roaming,” arXiv preprint arXiv:2402.00763, 2024

    2024 arXiv

  17. [25]

    Deblurring 3d gaussian splatting,

    B. Lee, H. Lee, X. Sun, U. Ali, and E. Park, “Deblurring 3d gaussian splatting,” arXiv preprint arXiv:2401.00834 , 2024

    2024 arXiv

  18. [26]

    Gscore: Efficient radiance field rendering via architectural support for 3d gaussian splatting,

    J. Lee, S. Lee, J. Lee, J. Park, and J. Sim, “Gscore: Efficient radiance field rendering via architectural support for 3d gaussian splatting,” in Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, V ol...

    2024

  19. [27]

    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,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 447–19 456

    2024

  20. [28]

    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,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 20 654–20 664

    2024

  21. [29]

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

    Y . Liu, H. Guan, C. Luo, L. Fan, J. Peng, and Z. Zhang, “Citygaussian: Real-time high-quality large-scale scene rendering with gaussians,” arXiv preprint arXiv:2404.01133, 2024

    2024 arXiv

  22. [30]

    4d gaussian splatting: Towards efficient novel view synthesis for dynamic scenes,

    Y . Duan, F. Wei, Q. Dai, Y . He, W. Chen, and B. Chen, “4d gaussian splatting: Towards efficient novel view synthesis for dynamic scenes,” arXiv preprint arXiv:2402.03307 , 2024

    2024 arXiv

  23. [31]

    Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction,

    Z. Yang, X. Gao, W. Zhou, S. Jiao, Y . Zhang, and X. Jin, “Deformable 3d gaussians for high-fidelity monocular dynamic scene reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 20 331–20 341

    2024

  24. [32]

    An efficient 3d gaussian representation for monocular/multi-view dynamic scenes,

    K. Katsumata, D. M. V o, and H. Nakayama, “An efficient 3d gaussian representation for monocular/multi-view dynamic scenes,” arXiv preprint arXiv:2311.12897, 2023

    2023 arXiv

  25. [33]

    Modgs: Dynamic gaussian splatting from causually-captured monocular videos,

    Q. Liu, Y . Liu, J. Wang, X. Lv, P. Wang, W. Wang, and J. Hou, “Modgs: Dynamic gaussian splatting from causually-captured monocular videos,” arXiv preprint arXiv:2406.00434 , 2024

    2024 arXiv

  26. [34]

    3d geometry-aware deformable gaussian splatting for dynamic view synthesis,

    Z. Lu, X. Guo, L. Hui, T. Chen, M. Yang, X. Tang, F. Zhu, and Y . Dai, “3d geometry-aware deformable gaussian splatting for dynamic view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 8900–8910

    2024

  27. [35]

    Motiongs: Exploring explicit motion guidance for deformable 3d gaussian splatting,

    R. Zhu, Y . Liang, H. Chang, J. Deng, J. Lu, W. Yang, T. Zhang, and Y . Zhang, “Motiongs: Exploring explicit motion guidance for deformable 3d gaussian splatting,” arXiv preprint arXiv:2410.07707 , 2024

    2024 arXiv

  28. [36]

    Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis,

    J. Luiten, G. Kopanas, B. Leibe, and D. Ramanan, “Dynamic 3d gaussians: Tracking by persistent dynamic view synthesis,” arXiv preprint arXiv:2308.09713, 2023

    2023 arXiv

  29. [37]

    Per-gaussian embedding-based deformation for deformable 3d gaussian splatting,

    J. Bae, S. Kim, Y . Yun, H. Lee, G. Bang, and Y . Uh, “Per-gaussian embedding-based deformation for deformable 3d gaussian splatting,” arXiv preprint arXiv:2404.03613 , 2024

    2024 arXiv

  30. [38]

    Gaussian-flow: 4d reconstruction with dynamic 3d gaussian particle,

    Y . Lin, Z. Dai, S. Zhu, and Y . Yao, “Gaussian-flow: 4d reconstruction with dynamic 3d gaussian particle,” in Proceedings of the IEEE/CVF CVPR, 2024, pp. 21 136–21 145

    2024

  31. [39]

    Spacetime gaussian feature splatting for real-time dynamic view synthesis,

    Z. Li, Z. Chen, Z. Li, and Y . Xu, “Spacetime gaussian feature splatting for real-time dynamic view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

    2024

  32. [40]

    Dynmf: Neural motion factorization for real-time dynamic view synthesis with 3d gaussian splatting,

    A. Kratimenos, J. Lei, and K. Daniilidis, “Dynmf: Neural motion factorization for real-time dynamic view synthesis with 3d gaussian splatting,” arXiv preprint arXiv:2312.00112 , 2023

    2023 arXiv

  33. [41]

    Sc- gs: Sparse-controlled gaussian splatting for editable dynamic scenes,

    Y .-H. Huang, Y .-T. Sun, Z. Yang, X. Lyu, Y .-P. Cao, and X. Qi, “Sc- gs: Sparse-controlled gaussian splatting for editable dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 4220–4230

    2024

  34. [42]

    Lgm: Large multi-view gaussian model for high-resolution 3d content creation,

    J. Tang, Z. Chen, X. Chen, T. Wang, G. Zeng, and Z. Liu, “Lgm: Large multi-view gaussian model for high-resolution 3d content creation,” in European Conference on Computer Vision . Springer, 2025, pp. 1–18

    2025

  35. [43]

    Gvgen: Text-to-3d generation with volumetric representation,

    X. He, J. Chen, S. Peng, D. Huang, Y . Li, X. Huang, C. Yuan, W. Ouyang, and T. He, “Gvgen: Text-to-3d generation with volumetric representation,” arXiv preprint arXiv:2403.12957 , 2024

    2024 arXiv

  36. [44]

    Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching,

    Y . Liang, X. Yang, J. Lin, H. Li, X. Xu, and Y . Chen, “Luciddreamer: Towards high-fidelity text-to-3d generation via interval score matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 6517–6526

    2024

  37. [45]

    3d gaussian model for animation and texturing,

    X. E. Wang and Z. Sin, “3d gaussian model for animation and texturing,” arXiv preprint arXiv:2402.19441 , 2024

    2024 arXiv

  38. [46]

    Langsplat: 3d language gaussian splatting,

    M. Qin, W. Li, J. Zhou, H. Wang, and H. Pfister, “Langsplat: 3d language gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 051–20 060

    2024

  39. [47]

    Dreamscene: 3d gaussian-based text-to-3d scene generation via formation pattern sampling,

    H. Li, H. Shi, W. Zhang, W. Wu, Y . Liao, L. Wang, L.-h. Lee, and P. Zhou, “Dreamscene: 3d gaussian-based text-to-3d scene generation via formation pattern sampling,” arXiv preprint arXiv:2404.03575 , 2024

    2024 arXiv

  40. [48]

    Dreamscene360: Unconstrained text-to- 3d scene generation with panoramic gaussian splatting,

    S. Zhou, Z. Fan, D. Xu, H. Chang, P. Chari, T. Bharadwaj, S. You, Z. Wang, and A. Kadambi, “Dreamscene360: Unconstrained text-to- 3d scene generation with panoramic gaussian splatting,” in European Conference on Computer Vision . Springer, 2025, pp. 324–342

    2025

  41. [49]

    Text2immersion: Generative immersive scene with 3d gaussians,

    H. Ouyang, K. Heal, S. Lombardi, and T. Sun, “Text2immersion: Generative immersive scene with 3d gaussians,” arXiv preprint arXiv:2312.09242, 2023

    2023 arXiv

  42. [50]

    Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion,

    J. Shriram, A. Trevithick, L. Liu, and R. Ramamoorthi, “Realmdreamer: Text-driven 3d scene generation with inpainting and depth diffusion,” arXiv preprint arXiv:2404.07199 , 2024

    2024 arXiv

  43. [51]

    Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting,

    X. Zhou, X. Ran, Y . Xiong, J. He, Z. Lin, Y . Wang, D. Sun, and M.- H. Yang, “Gala3d: Towards text-to-3d complex scene generation via layout-guided generative gaussian splatting,” arXiv preprint:2402.07207

    arXiv

  44. [52]

    Hifi4g: High-fidelity human performance rendering via compact gaussian splatting,

    Y . Jiang, Z. Shen, P. Wang, Z. Su, Y . Hong, Y . Zhang, J. Yu, and L. Xu, “Hifi4g: High-fidelity human performance rendering via compact gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 734–19 745

    2024

  45. [53]

    Rtg-slam: Real-time 3d reconstruction at scale using gaussian splatting,

    Z. Peng, T. Shao, Y . Liu, J. Zhou, Y . Yang, J. Wang, and K. Zhou, “Rtg-slam: Real-time 3d reconstruction at scale using gaussian splatting,” in ACM SIGGRAPH 2024 Conference Papers , 2024, pp. 1–11

    2024

  46. [54]

    3dgstream: On- the-fly training of 3d gaussians for efficient streaming of photo-realistic free-viewpoint videos,

    J. Sun, H. Jiao, G. Li, Z. Zhang, L. Zhao, and W. Xing, “3dgstream: On- the-fly training of 3d gaussians for efficient streaming of photo-realistic free-viewpoint videos,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 675–20 685

    2024

  47. [55]

    Headstudio: Text to animatable head avatars with 3d gaussian splatting,

    Z. Zhou, F. Ma, H. Fan, and Y . Yang, “Headstudio: Text to animatable head avatars with 3d gaussian splatting,” arXiv preprint:2402.06149

    arXiv

  48. [56]

    Compressed 3d gaussian splatting for accelerated novel view synthesis,

    S. Niedermayr, J. Stumpfegger, and R. Westermann, “Compressed 3d gaussian splatting for accelerated novel view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 10 349–10 358

    2024

  49. [57]

    Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians,

    S. Qian, T. Kirschstein, L. Schoneveld, D. Davoli, S. Giebenhain, and M. Nießner, “Gaussianavatars: Photorealistic head avatars with rigged 3d gaussians,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

    2024

  50. [58]

    Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting,

    Z. Yang, X. Gao, Y . Sun, Y . Huang, X. Lyu, W. Zhou, S. Jiao, X. Qi, and X. Jin, “Spec-gaussian: Anisotropic view-dependent appearance for 3d gaussian splatting,” arXiv preprint arXiv:2402.15870 , 2024

    2024 arXiv

  51. [59]

    Gs-ir: 3d gaussian splatting for inverse rendering,

    Z. Liang, Q. Zhang, Y . Feng, Y . Shan, and K. Jia, “Gs-ir: 3d gaussian splatting for inverse rendering,” in Proceedings of the IEEE/CVF CVPR , 2024, pp. 21 644–21 653

    2024

  52. [60]

    Stylegaus- sian: Instant 3d style transfer with gaussian splatting,

    K. Liu, F. Zhan, M. Xu, C. Theobalt, L. Shao, and S. Lu, “Stylegaus- sian: Instant 3d style transfer with gaussian splatting,” arXiv preprint arXiv:2403.07807, 2024

    2024 arXiv

  53. [61]

    Gaussian splatting in style,

    A. Saroha, M. Gladkova, C. Curreli, T. Yenamandra, and D. Cremers, “Gaussian splatting in style,” arXiv preprint arXiv:2403.08498 , 2024

    2024 arXiv

  54. [62]

    Loopgaussian: Creating 3d cinemagraph with multi-view images via eulerian motion field,

    J. Li, L. Cheng, Z. Wang, T. Mu, and J. He, “Loopgaussian: Creating 3d cinemagraph with multi-view images via eulerian motion field,” arXiv preprint arXiv:2404.08966, 2024

    2024 arXiv

  55. [63]

    Cogs: Controllable gaussian splatting,

    H. Yu, J. Julin, Z. ´A. Milacski, K. Niinuma, and L. A. Jeni, “Cogs: Controllable gaussian splatting,” in Proceedings of the IEEE/CVF CVPR , 2024, pp. 21 624–21 633

    2024

  56. [64]

    View- consistent 3d editing with gaussian splatting,

    Y . Wang, X. Yi, Z. Wu, N. Zhao, L. Chen, and H. Zhang, “View- consistent 3d editing with gaussian splatting,” in European Conference on Computer Vision . Springer, 2025, pp. 404–420

    2025

  57. [65]

    Ice-g: Image conditional editing of 3d gaussian splats,

    V . Jaganathan, H. H. Huang, M. Z. Irshad, V . Jampani, A. Raj, and Z. Kira, “Ice-g: Image conditional editing of 3d gaussian splats,” arXiv preprint arXiv:2406.08488, 2024

    2024 arXiv

  58. [66]

    Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,

    Y . Chen, Z. Chen, C. Zhang, F. Wang, X. Yang, Y . Wang, Z. Cai, L. Yang, H. Liu, and G. Lin, “Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 476–21 485

    2024

  59. [67]

    Gaussianeditor: Editing 3d gaussians delicately with text instructions,

    J. Wang, J. Fang, X. Zhang, L. Xie, and Q. Tian, “Gaussianeditor: Editing 3d gaussians delicately with text instructions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 902–20 911

    2024

  60. [68]

    Rig3dgs: Creating controllable portraits from casual monocular videos,

    A. Rivero, S. Athar, Z. Shu, and D. Samaras, “Rig3dgs: Creating controllable portraits from casual monocular videos,” arXiv preprint arXiv:2402.03723, 2024

    2024 arXiv

  61. [69]

    Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps,

    Z. Fan, K. Wang, K. Wen, Z. Zhu, D. Xu, and Z. Wang, “Lightgaussian: Unbounded 3d gaussian compression with 15x reduction and 200+ fps,” arXiv preprint arXiv:2311.17245 , 2023

    2023 arXiv

  62. [70]

    Gaussian shell maps for efficient 3d human generation,

    R. Abdal, W. Yifan, Z. Shi, Y . Xu, R. Po, Z. Kuang, Q. Chen, D.-Y . Yeung, and G. Wetzstein, “Gaussian shell maps for efficient 3d human generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 9441–9451

    2024

  63. [71]

    Compgs: Smaller and faster gaussian splatting with vector quantization,

    K. Navaneet, K. P. Meibodi, S. A. Koohpayegani, and H. Pirsiavash, “Compgs: Smaller and faster gaussian splatting with vector quantization,” in European Conference on Computer Vision , 2024

    2024

  64. [72]

    Gsdf: 3dgs meets sdf for improved rendering and reconstruction,

    M. Yu, T. Lu, L. Xu, L. Jiang, Y . Xiangli, and B. Dai, “Gsdf: 3dgs meets sdf for improved rendering and reconstruction,” arXiv preprint arXiv:2403.16964, 2024

    2024 arXiv

  65. [73]

    Neds-slam: A novel neural explicit dense semantic slam framework using 3d gaussian splatting,

    Y . Ji, Y . Liu, G. Xie, B. Ma, and Z. Xie, “Neds-slam: A novel neural explicit dense semantic slam framework using 3d gaussian splatting,” arXiv preprint arXiv:2403.11679 , 2024

    2024 arXiv

  66. [74]

    High-fidelity slam using gaussian splatting with rendering-guided densification and regularized optimization,

    S. Sun, M. Mielle, A. J. Lilienthal, and M. Magnusson, “High-fidelity slam using gaussian splatting with rendering-guided densification and regularized optimization,” arXiv preprint arXiv:2403.12535 , 2024

    2024 arXiv

  67. [75]

    Cg-slam: Efficient dense rgb-d slam in a consistent uncertainty-aware 3d gaussian field,

    J. Hu, X. Chen, B. Feng, G. Li, L. Yang, H. Bao, G. Zhang, and Z. Cui, “Cg-slam: Efficient dense rgb-d slam in a consistent uncertainty-aware 3d gaussian field,” arXiv preprint arXiv:2403.16095 , 2024

    2024 arXiv

  68. [76]

    Mm3dgs slam: Multi-modal 3d gaussian splatting for slam using vision, depth, and inertial measurements,

    L. C. Sun, N. P. Bhatt, J. C. Liu, Z. Fan, Z. Wang, T. E. Humphreys, and U. Topcu, “Mm3dgs slam: Multi-modal 3d gaussian splatting for slam using vision, depth, and inertial measurements,” arXiv preprint arXiv:2404.00923, 2024

    2024 arXiv

  69. [77]

    Gs-slam: Dense visual slam with 3d gaussian splatting,

    C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li, “Gs-slam: Dense visual slam with 3d gaussian splatting,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 595–19 604

    2024

  70. [78]

    Splatam: Splat track & map 3d gaussians for dense rgb-d slam,

    N. Keetha, J. Karhade, K. M. Jatavallabhula, G. Yang, S. Scherer, D. Ramanan, and J. Luiten, “Splatam: Splat track & map 3d gaussians for dense rgb-d slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 357–21 366

    2024

  71. [79]

    Manus: Markerless hand-object grasp capture using articulated 3d gaussians,

    C. Pokhariya and N. Ishaan, “Manus: Markerless hand-object grasp capture using articulated 3d gaussians,” in IEEE/CVF CVPR , 2024

    2024

  72. [80]

    Gs-pose: Cascaded framework for generalizable segmentation-based 6d object pose estimation,

    D. Cai, J. Heikkil ¨a, and E. Rahtu, “Gs-pose: Cascaded framework for generalizable segmentation-based 6d object pose estimation,” arXiv preprint arXiv:2403.10683, 2024

    2024 arXiv

  73. [81]

    Ggrt: Towards generalizable 3d gaussians without pose priors in real-time,

    H. Li, Y . Gao, D. Zhang, C. Wu, Y . Dai, C. Zhao, H. Feng, E. Ding, J. Wang, and J. Han, “Ggrt: Towards generalizable 3d gaussians without pose priors in real-time,” arXiv preprint arXiv:2403.10147 , 2024

    2024 arXiv

  74. [82]

    Endogaussian: Gaussian splatting for deformable surgical scene reconstruction,

    Y . Liu, C. Li, C. Yang, and Y . Yuan, “Endogaussian: Gaussian splatting for deformable surgical scene reconstruction,” arXiv preprint arXiv:2401.12561, 2024

    2024 arXiv

  75. [83]

    Gaussian pancakes: Geometrically-regularized 3d gaussian splatting for realistic endoscopic reconstruction,

    S. Bonilla, S. Zhang, D. Psychogyios, D. Stoyanov, F. Vasconcelos, and S. Bano, “Gaussian pancakes: Geometrically-regularized 3d gaussian splatting for realistic endoscopic reconstruction,” in MICCAI, 2024

    2024

  76. [84]

    Application of 3d gaussian splatting for cinematic anatomy on consumer class devices,

    S. Niedermayr, C. Neuhauser, K. Petkov, K. Engel, and R. Westermann, “Application of 3d gaussian splatting for cinematic anatomy on consumer class devices,” 2024

    2024

  77. [85]

    Xpbd: position-based simulation of compliant constrained dynamics,

    M. Macklin, M. M ¨uller, and N. Chentanez, “Xpbd: position-based simulation of compliant constrained dynamics,” in Proceedings of the 9th International Conference on Motion in Games , 2016, pp. 49–54

    2016

  78. [86]

    Smpl: A skinned multi-person linear model,

    M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “Smpl: A skinned multi-person linear model,” in Seminal Graphics Papers: Pushing the Boundaries, V olume 2 , 2023, pp. 851–866

    2023

  79. [87]

    Progress and prospects in 3d generative ai: A technical overview including 3d human,

    S. Bai and J. Li, “Progress and prospects in 3d generative ai: A technical overview including 3d human,” arXiv preprint arXiv:2401.02620 , 2024

    2024 arXiv

  80. [88]

    A survey on 3d gaussian splatting,

    G. Chen and W. Wang, “A survey on 3d gaussian splatting,” arXiv preprint arXiv:2401.03890, 2024

    2024 arXiv

  81. [89]

    3d gaussian splatting as new era: A survey,

    B. Fei, J. Xu, R. Zhang, Q. Zhou, W. Yang, and Y . He, “3d gaussian splatting as new era: A survey,” IEEE Transactions on Visualization and Computer Graphics , p. 1–20, 2024

    2024

  82. [90]

    How nerfs and 3d gaussian splatting are reshaping slam: a survey,

    F. Tosi, Y . Zhang, Z. Gong, E. Sandstr ¨om, S. Mattoccia, M. R. Oswald, and M. Poggi, “How nerfs and 3d gaussian splatting are reshaping slam: a survey,” arXiv preprint arXiv:2402.13255 , vol. 4, 2024

    2024 arXiv

  83. [91]

    Recent advances in 3d gaussian splatting,

    T. Wu, Y .-J. Yuan, L.-X. Zhang, J. Yang, Y .-P. Cao, L.-Q. Yan, and L. Gao, “Recent advances in 3d gaussian splatting,” Computational Visual Media, vol. 10, no. 4, pp. 613–642, 2024

    2024

  84. [92]

    Gaussian splatting: 3d reconstruction and novel view synthesis: A review,

    A. Dalal, D. Hagen, K. G. Robbersmyr, and K. M. Knausg ˚ard, “Gaussian splatting: 3d reconstruction and novel view synthesis: A review,” IEEE Access, vol. 12, p. 96797–96820, 2024

    2024

  85. [93]

    Energy-based residual latent transport for unsupervised point cloud completion,

    R. Cui, S. Qiu, S. Anwar, J. Zhang, and N. Barnes, “Energy-based residual latent transport for unsupervised point cloud completion,” BMVC, 2022

    2022

  86. [94]

    Ssp: Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds,

    R. Zhu, D. Kang, K.-H. Hui, Y . Qian, S. Qiu, Z. Dong, L. Bao, P.-A. Heng, and C.-W. Fu, “Ssp: Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds,” in WACV, 2024, pp. 3769–3778

    2024

  87. [95]

    Gs-vton: Controllable 3d virtual try-on with gaussian splatting,

    Y . Cao, M. Hadi, L. Pan, and Z. Liu, “Gs-vton: Controllable 3d virtual try-on with gaussian splatting,” arXiv preprint arXiv:2410.05259 , 2024

    2024 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.