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arxiv: 2606.03254 · v1 · pith:BQ2L25QLnew · submitted 2026-06-02 · 💻 cs.CV

FreeStreamGS: Online Feed-forward 3D Gaussian Splatting from Unposed Streaming Inputs

Ruiyang Chen , Feiran Li , Chu Zhou , Zonglin Li , Zhanyu Ma , Heng Guo This is my paper

Pith reviewed 2026-06-28 10:39 UTC · model grok-4.3

classification 💻 cs.CV
keywords 3D Gaussian Splattingonline novel view synthesisfeed-forward reconstructionunposed streaming imagesmulti-view consistencycamera intrinsic recoverypose-depth drift correction
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The pith

FreeStreamGS performs online 3D Gaussian Splatting from unposed streaming images at quality levels competitive with offline methods.

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

The paper presents FreeStreamGS as an online feed-forward system that generates novel views from a continuous stream of unposed images without waiting for future frames. It targets the problem that small inconsistencies in Gaussian scales or pose-geometry alignment quickly accumulate into visible rendering artifacts in long sequences. Two mechanisms are introduced to enforce consistency: one that decouples and corrects camera intrinsic bias to stop scale jitter, and another that adds a dynamic offset to refine points and correct pose-depth drift. If successful, this would allow real-time high-fidelity rendering directly from live camera input. A sympathetic reader would care because it removes the offline recording requirement that currently limits 3DGS to post-capture use.

Core claim

FreeStreamGS is a robust online feed-forward framework for efficient and high-quality novel view synthesis. It introduces a Decoupled Intrinsic Recovery Head that removes cumulative camera intrinsic bias and prevents scene scale jitter during long-term streaming, and a Dynamic Point Refinement Offset strategy that relaxes rigid unprojection to correct coupled pose-depth drift. Extensive experiments show that FreeStreamGS achieves rendering quality competitive with state-of-the-art offline feed-forward 3DGS methods, despite operating without access to future frames.

What carries the argument

Decoupled Intrinsic Recovery Head and Dynamic Point Refinement Offset, which together enforce multi-view consistency in Gaussian scales and pose-geometry alignment for streaming inputs.

If this is right

  • Online novel view synthesis becomes feasible from unposed streaming inputs without future frames.
  • Cumulative camera intrinsic bias no longer produces scene scale jitter in extended streams.
  • Pose-depth drift can be corrected without rigid unprojection, preserving rendering fidelity.
  • Rendering quality remains competitive with offline methods when the two mechanisms are applied.

Where Pith is reading between the lines

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

  • The approach could support live applications such as real-time AR overlays if paired with low-latency pose estimation.
  • The consistency fixes might transfer to other streaming 3D reconstruction tasks beyond Gaussian splatting.
  • Longer test sequences would reveal whether drift correction remains stable beyond the evaluated lengths.

Load-bearing premise

The two new mechanisms maintain multi-view consistency in Gaussian scales and pose-geometry alignment over long streams without introducing new artifacts or needing future frames.

What would settle it

A long unposed image stream on which FreeStreamGS produces visibly worse rendering artifacts or scale jitter than an offline feed-forward 3DGS baseline after several hundred frames.

Figures

Figures reproduced from arXiv: 2606.03254 by Chu Zhou, Feiran Li, Heng Guo, Ruiyang Chen, Zhanyu Ma, Zonglin Li.

Figure 1
Figure 1. Figure 1: FreeStreamGS is a feed-forward framework that incrementally reconstructs high-quality 3D Gaussians from unposed streaming image sequences, enabling low￾latency and high-quality online novel view synthesis (NVS). Abstract. Feed-forward 3D Gaussian Splatting (3DGS) allows efficient and high-fidelity novel view synthesis (NVS) from an offline recorded image sequence. However, achieving online NVS from streami… view at source ↗
Figure 2
Figure 2. Figure 2: Two geometric inconsistency in online 3DGS. (Left) Intrinsics drift induces global scale inconsistency. (Right) Rigid viewing-ray constraints lead to distorted Gaus￾sian placement under coupled errors. To address these challenges, FreeStreamGS incorporates two synergistic mech￾anisms designed to restore geometric stability in causal streaming. First, we propose Decoupled Intrinsic Recovery Head (DIR-Head).… view at source ↗
Figure 3
Figure 3. Figure 3: Overview of FreeStreamGS. A causal extractor with KV caching leverages historical context from unposed streams. Decoupled camera recovery heads then predict camera parameters (Causal Extrinsic Head & DIR-Head) and 3D Gaussian primitives (Gaussian decoder) refined by DPR-Offsets to rectify spatial drift. These primitives are consolidated via Online Recursive Gaussian Fusion for rendering. Training utilizes … view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison on DL3DV-140 [15] (top two rows) and RE10K [33] (bottom two rows) datasets. Our method produces high-quality novel view renderings while preserving fine structural details. forward online reconstruction outperforms the optimization-based OnTheFly￾NVS [18] and shows comparable performance with the SOTA offline reconstruc￾tion method. These results suggest that explicitly addressing ge… view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison on unseen NYUv2 [19] test dataset. FreeStreamGS demonstrates robust cross-dataset generalization without additional fine-tuning [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Application of FreeStreamGS on online full-frame video stabilization (tested on 150-frame sequences). Top: reference frames with red lines marking sampled columns. Bottom: temporal profiles (X-T slices), generated by horizontally concatenating the sampled column across all frames. DPR-Offsets leads to noticeable performance degradation, highlighting DPR￾Offsets for providing spatial flexibility through 3D … view at source ↗
read the original abstract

Feed-forward 3D Gaussian Splatting (3DGS) allows efficient and high-fidelity novel view synthesis (NVS) from an offline recorded image sequence. However, achieving online NVS from streaming and unposed image inputs remains challenging. Although online feed-forward geometric estimation methods have been proposed for streaming depth and point cloud recovery, they cannot be adapted to NVS due to severe rendering artifacts. This is because NVS demands stricter multi-view consistency in Gaussian scales and pose-geometry alignment; even minor deviations would accumulate over time and visibly degrade rendering quality. To this end, we propose FreeStreamGS, a robust online feed-forward framework for efficient and high-quality NVS. We introduce two key mechanisms: a Decoupled Intrinsic Recovery Head that removes cumulative camera intrinsic bias and prevents scene scale jitter during long-term streaming, and a Dynamic Point Refinement Offset strategy that relaxes rigid unprojection to correct coupled pose-depth drift. Extensive experiments show that FreeStreamGS achieves rendering quality competitive with state-of-the-art offline feed-forward 3DGS methods, despite operating without access to future frames.

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

3 major / 2 minor

Summary. The paper introduces FreeStreamGS, an online feed-forward framework for 3D Gaussian Splatting from unposed streaming image inputs. It proposes two mechanisms—a Decoupled Intrinsic Recovery Head to eliminate cumulative camera intrinsic bias and prevent long-term scene scale jitter, and a Dynamic Point Refinement Offset strategy to relax rigid unprojection and correct coupled pose-depth drift—to enforce the multi-view consistency in Gaussian scales and pose-geometry alignment required for high-quality novel view synthesis. The central claim, supported by extensive experiments, is that FreeStreamGS achieves rendering quality competitive with state-of-the-art offline feed-forward 3DGS methods while operating without access to future frames.

Significance. If the two proposed mechanisms demonstrably sustain multi-view consistency over long streams without introducing compensating artifacts or implicit future-frame dependence, the work would meaningfully advance online NVS, enabling practical deployment in streaming scenarios where offline batch methods are inapplicable. The explicit identification of scale jitter and coupled drift as the precise failure modes of prior geometric approaches, together with targeted architectural fixes, strengthens the contribution if the empirical validation holds.

major comments (3)
  1. [Abstract, §4] Abstract and §4 (experiments): the claim of 'competitive' rendering quality with offline SOTA methods is asserted but the abstract provides no quantitative metrics, baselines, error bars, or dataset details; without these, the central claim that the two mechanisms succeed in preventing accumulation of deviations cannot be evaluated.
  2. [§3.2] §3.2 (Decoupled Intrinsic Recovery Head): the mechanism is described as removing cumulative intrinsic bias, but no derivation or ablation is referenced showing that the head operates without access to future frames or without reintroducing scale jitter over sequences longer than those tested; this is load-bearing for the no-future-frames claim.
  3. [§3.3] §3.3 (Dynamic Point Refinement Offset): the strategy relaxes rigid unprojection to correct pose-depth drift, yet the manuscript does not report quantitative tracking of multi-view consistency (e.g., scale variance or alignment error) across streaming length, leaving the assumption that it prevents visible degradation unverified.
minor comments (2)
  1. [§3.3] Notation for the refinement offset should be defined explicitly with respect to the unprojection equation to avoid ambiguity in how the relaxation is parameterized.
  2. [Figures 4-6] Figure captions for qualitative results should include the exact sequence length and number of frames to allow readers to assess long-term stability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below and will revise the manuscript to strengthen the presentation of quantitative results and validation of the proposed mechanisms.

read point-by-point responses

  1. Referee: [Abstract, §4] Abstract and §4 (experiments): the claim of 'competitive' rendering quality with offline SOTA methods is asserted but the abstract provides no quantitative metrics, baselines, error bars, or dataset details; without these, the central claim that the two mechanisms succeed in preventing accumulation of deviations cannot be evaluated.

    Authors: The detailed quantitative results, including metrics, baselines, error bars, and dataset information, are presented in Section 4. However, we agree that the abstract would benefit from including key quantitative highlights to support the claim. We will revise the abstract to incorporate representative metrics from our experiments. revision: yes

  2. Referee: [§3.2] §3.2 (Decoupled Intrinsic Recovery Head): the mechanism is described as removing cumulative intrinsic bias, but no derivation or ablation is referenced showing that the head operates without access to future frames or without reintroducing scale jitter over sequences longer than those tested; this is load-bearing for the no-future-frames claim.

    Authors: The architecture of the Decoupled Intrinsic Recovery Head is explicitly feed-forward and processes frames sequentially without future information, as described in §3.2 and illustrated in the method figure. To strengthen this, we will add a derivation explaining the decoupling and an ablation study on extended sequence lengths to verify the prevention of scale jitter. revision: yes

  3. Referee: [§3.3] §3.3 (Dynamic Point Refinement Offset): the strategy relaxes rigid unprojection to correct pose-depth drift, yet the manuscript does not report quantitative tracking of multi-view consistency (e.g., scale variance or alignment error) across streaming length, leaving the assumption that it prevents visible degradation unverified.

    Authors: While the overall rendering quality in long streaming scenarios is demonstrated in our experiments, we acknowledge the value of direct quantitative tracking. We will include additional analysis and metrics for multi-view consistency (such as scale variance and alignment errors) over varying streaming lengths in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical validation of independent mechanisms

full rationale

The paper introduces two new architectural components (Decoupled Intrinsic Recovery Head and Dynamic Point Refinement Offset) to solve identified failure modes in online feed-forward 3DGS, then reports competitive rendering quality from experiments on streaming inputs. No derivation, prediction, or first-principles result is claimed that reduces by construction to fitted parameters, self-citations, or renamed inputs. The central claim rests on empirical comparison rather than any self-referential loop, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no equations, training details, or implementation sections from which to extract free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5736 in / 989 out tokens · 20998 ms · 2026-06-28T10:39:48.958620+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

34 extracted references · 13 canonical work pages · 4 internal anchors

  1. [1]

    Charatan, D., Li, S.L., Tagliasacchi, A., Sitzmann, V.: Pixelsplat: 3d gaussian splatsfromimagepairsforscalablegeneralizable3dreconstruction.In:Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 19457–19467 (2024)

    2024

  2. [2]

    In: European Conference on Computer Vision

    Chen, Y., Xu, H., Zheng, C., Zhuang, B., Pollefeys, M., Geiger, A., Cham, T.J., Cai, J.: Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images. In: European Conference on Computer Vision. pp. 370–386. Springer (2024)

    2024

  3. [3]

    Salon3r: Structure-aware long-term generalizable 3d reconstruction from unposed images.arXiv preprint arXiv:2510.15072,

    Guo, J., Guan, T., Dong, W., Zheng, W., Wang, W., Wang, Y., Yam, Y., Liu, Y.H.: Salon3r: Structure-aware long-term generalizable 3d reconstruction from unposed images. arXiv preprint arXiv:2510.15072 (2025)

    work page arXiv 2025

  4. [4]

    arXiv preprint arXiv:2410.22128 (2024) 3, 8

    Hong, S., Jung, J., Shin, H., Han, J., Yang, J., Luo, C., Kim, S.: Pf3plat: Pose-free feed-forward 3d gaussian splatting. arXiv preprint arXiv:2410.22128 (2024)

    work page arXiv 2024

  5. [5]

    arXiv preprint arXiv:2507.16144 (2025) 22 R

    Huang, G., Wang, R., Gao, X., Sun, C., Wu, Y., Gao, S., Jia, Y.: Longsplat: On- line generalizable 3d gaussian splatting from long sequence images. arXiv preprint arXiv:2507.16144 (2025) 22 R. Chen et al

    work page arXiv 2025

  6. [6]

    ACM Transactions on Graphics (TOG)44(6), 1–16 (2025)

    Jiang, L., Mao, Y., Xu, L., Lu, T., Ren, K., Jin, Y., Xu, X., Yu, M., Pang, J., Zhao, F., et al.: Anysplat: Feed-forward 3d gaussian splatting from unconstrained views. ACM Transactions on Graphics (TOG)44(6), 1–16 (2025)

    2025

  7. [7]

    In: ACM SIGGRAPH 2024 conference papers

    Jiang, Y., Yu, C., Xie, T., Li, X., Feng, Y., Wang, H., Li, M., Lau, H., Gao, F., Yang, Y., et al.: Vr-gs: A physical dynamics-aware interactive gaussian splatting system in virtual reality. In: ACM SIGGRAPH 2024 conference papers. pp. 1–1 (2024)

    2024

  8. [8]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Keetha, N., Karhade, J., Jatavallabhula, K.M., Yang, G., Scherer, S., Ramanan, D., Luiten, J.: Splatam: Splat track & map 3d gaussians for dense rgb-d slam. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 21357–21366 (2024)

    2024

  9. [9]

    In: International Conference on Learning Representations (2026)

    Lan,Y.,Luo,Y.,Hong,F.,Zhou,S.,Chen,H.,Lyu,Z.,Yang,S.,Dai,B.,Loy,C.C., Pan, X.: STream3R: Scalable sequential 3D reconstruction with causal transformer. In: International Conference on Learning Representations (2026)

    2026

  10. [10]

    arXiv preprint arXiv:2510.08551 , year=

    Li, G., Ren, K., Xu, L., Zheng, Z., Jiang, C., Gao, X., Dai, B., Pu, J., Yu, M., Pang, J.: Artdeco: Towards efficient and high-fidelity on-the-fly 3d reconstruction with structured scene representation. arXiv preprint arXiv:2510.08551 (2025)

    work page arXiv 2025

  11. [11]

    arXiv preprint arXiv:2503.06235 (2025)

    Li, Y., Wang, J., Chu, L., Li, X., Kao, S.h., Chen, Y.C., Lu, Y.: Streamgs: Online generalizable gaussian splatting reconstruction for unposed image streams. arXiv preprint arXiv:2503.06235 (2025)

    work page arXiv 2025

  12. [12]

    arXiv preprint arXiv:2503.10286 (2025)

    Li, Z., Dong, C., Chen, Y., Huang, Z., Liu, P.: Vicasplat: A single run is all you need for 3d gaussian splatting and camera estimation from unposed video frames. arXiv preprint arXiv:2503.10286 (2025)

    work page arXiv 2025

  13. [13]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Lin, C.Y., Sun, C., Yang, F.E., Chen, M.H., Lin, Y.Y., Liu, Y.L.: Longsplat: Ro- bust unposed 3d gaussian splatting for casual long videos. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 27412–27422 (2025)

    2025

  14. [14]

    Depth Anything 3: Recovering the Visual Space from Any Views

    Lin, H., Chen, S., Liew, J.H., Chen, D.Y., Li, Z., Shi, G., Feng, J., Kang, B.: Depth anything 3: Recovering the visual space from any views. arXiv preprint arXiv:2511.10647 (2025)

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  15. [15]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Ling, L., Sheng, Y., Tu, Z., Zhao, W., Xin, C., Wan, K., Yu, L., Guo, Q., Yu, Z., Lu, Y., et al.: Dl3dv-10k: A large-scale scene dataset for deep learning-based 3d vision. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 22160–22169 (2024)

    2024

  16. [16]

    Liu, Y., Min, Z., Wang, Z., Wu, J., Wang, T., Yuan, Y., Luo, Y., Guo, C.: World- mirror:Universal3dworldreconstructionwithany-priorprompting.arXivpreprint arXiv:2510.10726 (2025)

    work page arXiv 2025

  17. [17]

    Decoupled Weight Decay Regularization

    Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  18. [18]

    ACM Transactions on Graphics (TOG)44(4) (2025)

    Meuleman, A., Shah, I., Lanvin, A., Kerbl, B., Drettakis, G.: On-the-fly reconstruc- tion for large-scale novel view synthesis from unposed images. ACM Transactions on Graphics (TOG)44(4) (2025)

    2025

  19. [19]

    In: European Conference on Computer Vision (2012)

    Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: Indoor segmentation and sup- port inference from rgbd images. In: European Conference on Computer Vision (2012)

    2012

  20. [20]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Ranftl, R., Bochkovskiy, A., Koltun, V.: Vision transformers for dense prediction. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 12179–12188 (2021)

    2021

  21. [21]

    Analytical chemistry36(8), 1627–1639 (1964) FreeStreamGS 23

    Savitzky, A., Golay, M.J.: Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry36(8), 1627–1639 (1964) FreeStreamGS 23

    1964

  22. [22]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., Novotny, D.: Vggt: Visual geometry grounded transformer. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 5294–5306 (2025)

    2025

  23. [23]

    Advances in Neural Infor- mation Processing Systems37, 107326–107349 (2024)

    Wang, Y., Huang, T., Chen, H., Lee, G.H.: Freesplat: Generalizable 3d gaussian splatting towards free view synthesis of indoor scenes. Advances in Neural Infor- mation Processing Systems37, 107326–107349 (2024)

    2024

  24. [24]

    arXiv preprint arXiv:2506.08862 (2025)

    Wu, Z., Yan, Q., Yi, X., Wang, L., Liao, R.: Streamsplat: Towards online dynamic 3d reconstruction from uncalibrated video streams. arXiv preprint arXiv:2506.08862 (2025)

    work page arXiv 2025

  25. [25]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Xie, T., Zong, Z., Qiu, Y., Li, X., Feng, Y., Yang, Y., Jiang, C.: Physgaussian: Physics-integrated 3d gaussians for generative dynamics. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 4389–4398 (2024)

    2024

  26. [26]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Xu, H., Peng, S., Wang, F., Blum, H., Barath, D., Geiger, A., Pollefeys, M.: Depth- splat: Connecting gaussian splatting and depth. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 16453–16463 (2025)

    2025

  27. [27]

    IEEE Robotics and Automation Letters11(1), 426–433 (2025)

    Xu, Y., Yu, Y., Gan, W., Wang, T., Zhan, Z., Cheng, H., Wang, X.: Gaussian on-the-fly splatting: A progressive framework for robust near real-time 3dgs opti- mization. IEEE Robotics and Automation Letters11(1), 426–433 (2025)

    2025

  28. [28]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Yan, C., Qu, D., Xu, D., Zhao, B., Wang, Z., Wang, D., Li, X.: Gs-slam: Dense visual slam with 3d gaussian splatting. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 19595–19604 (2024)

    2024

  29. [29]

    No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images.arXiv preprint arXiv:2410.24207, 2024

    Ye, B., Liu, S., Xu, H., Li, X., Pollefeys, M., Yang, M.H., Peng, S.: No pose, no problem: Surprisingly simple 3d gaussian splats from sparse unposed images. arXiv preprint arXiv:2410.24207 (2024)

    work page arXiv 2024

  30. [30]

    In: Pro- ceedings of the Special Interest Group on Computer Graphics and Interactive Tech- niques Conference Conference Papers

    You, Z., Georgoulis, S., Chen, A., Tang, S., Dai, D.: Gavs: 3d-grounded video stabilization via temporally-consistent local reconstruction and rendering. In: Pro- ceedings of the Special Interest Group on Computer Graphics and Interactive Tech- niques Conference Conference Papers. pp. 1–12 (2025)

    2025

  31. [31]

    In: Proceedings of the Computer Vision and Pattern Recognition Conference

    Zhang, S., Wang, J., Xu, Y., Xue, N., Rupprecht, C., Zhou, X., Shen, Y., Wet- zstein, G.: Flare: Feed-forward geometry, appearance and camera estimation from uncalibrated sparse views. In: Proceedings of the Computer Vision and Pattern Recognition Conference. pp. 21936–21947 (2025)

    2025

  32. [32]

    In: Proceedings of the Computer Vision and Pattern Recognition Confer- ence

    Zheng, S., Zhou, B., Shao, R., Liu, B., Zhang, S., Nie, L., Liu, Y.: Gps-gaussian: Generalizable pixel-wise 3d gaussian splatting for real-time human novel view syn- thesis. In: Proceedings of the Computer Vision and Pattern Recognition Confer- ence. pp. 19680–19690 (2024)

    2024

  33. [33]

    Zhou, T., Tucker, R., Flynn, J., Fyffe, G., Snavely, N.: Stereo magnification: Learn- ingviewsynthesisusingmultiplaneimages.arXivpreprintarXiv:1805.09817(2018)

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  34. [34]

    Streaming 4D Visual Geometry Transformer

    Zhuo, D., Zheng, W., Guo, J., Wu, Y., Zhou, J., Lu, J.: Streaming 4d visual geometry transformer. arXiv preprint arXiv:2507.11539 (2025) 24 R. Chen et al. GT Ours OnTheFly- NVS [18] World Mirror [16] AnySplat [6] FLARE [31] Fig. E.5:Qualitative comparison on DL3DV-140 [15] datasets under 10 input views. FreeStreamGS 25 GT Ours OnTheFly- NVS [18] World Mir...

    work page internal anchor Pith review Pith/arXiv arXiv 2025