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REVIEW 4 major objections 4 minor 44 references

Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Multi-view geometric priors improve 3DGS reconstruction only when weighted by confidence.

desk verdict Plausible confidence-weighting recipe for multi-view priors in 3DGS, but the key ablation is missing on the dataset where it matters most. read the letter →

arxiv 2608.06117 v1 pith:6V7CHPII submitted 2026-08-06 cs.CV cs.GR

Hongyu Zhou , Zorah Lähner This is my paper
classification cs.CVcs.GR
keywords 3DGaussianSplattingmulti-viewgeometricpriorssurfacereconstructionconfidencemapsspecularobjectsdepthestimationnormal
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 tries to show that the right way to inject geometric priors into 3D Gaussian splatting is to use multi-view rather than monocular predictions, and to let the predictor's own confidence map decide how much each pixel's depth and normal constraint counts. On shiny, reflective objects, where photometric losses let 3DGS produce plausible images but wrong surfaces, this recipe lowers the mean normal error on Shiny Blender from 3.23 degrees for the PGSR baseline to 1.23 degrees, without hurting rendering PSNR. The authors argue that confidence weighting is not a nicety but the load-bearing ingredient: ablating it makes the priors net-harmful on DTU and Tanks and Temples. If true, it gives existing GS reconstruction pipelines a plug-in regularizer that recovers geometry on specular scenes at essentially no rendering cost.

What carries the argument

The central object is VGGT's per-pixel confidence map $C$, normalized and raised to a decaying power $f(k) = (k-3000)/3000$ to form $W_{\text{conf}} = C^{f(k)}$. This weight multiplies the normal and depth residuals in the geometric regularization loss $L_{\text{geo}}$, so unreliable predictions, typically on glossy side surfaces and occlusions, are down-weighted while confident ones dominate. Confidence also drives the affine alignment of predicted depth to rendered depth: the alignment is computed only on pixels where $C > 0.5$, avoiding error-prone estimates. The regularization is added on top of PGSR's own planar-based geometric losses.

What would settle it

Re-run the Shiny Blender experiments replacing the confidence map with uniform weights or with an inverted confidence map. If either variant matches the 1.23-degree mean normal error of the full method, then confidence-based down-weighting of unreliable pixels is not what drives the gain; the paper's own w/o-confidence ablation predicts that both variants should land near or above the 3.23-degree PGSR baseline.

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Extended reading notes

Core claim

Geometric priors from a multi-view vision transformer (VGGT), in the form of depth and normal maps, improve the surface geometry recovered by 3D Gaussian splatting, but only when each prior prediction is weighted by the confidence map VGGT outputs alongside it. The paper shows on Shiny Blender that confidence-weighted multi-view priors reduce mean normal MAE from 3.23 degrees for the PGSR base to 1.23 degrees, while PSNR stays essentially unchanged (28.07 to 28.05). The ablation makes the mechanism explicit: using VGGT priors without confidence weighting degrades DTU chamfer distance from 0.52 to 0.57 and TnT F1 from 0.40 to 0.37, whereas the full method improves both, so the confidence map is what converts a prior that is often wrong in detail into a selective supervisor that only constrains geometry where the multi-view predictions agree.

Load-bearing premise

Everything depends on VGGT's confidence map being genuinely calibrated, meaning low-confidence pixels are actually the ones where depth and normal predictions are wrong; the paper trusts this internal uncertainty rather than calibrating it to the target scenes.

Editorial extensions

If this is right

  • The same confidence-weighted regularization can be plugged into other GS-based reconstruction methods, not just PGSR, since it only adds a loss term and an alignment step.
  • On Lambertian scenes like DTU and most of TnT the gains are small; the benefit concentrates on specular and multi-object scenes, so future reconstruction benchmarks should report shiny-object geometry separately.
  • Rendering quality is not traded away for geometry: adding the priors leaves PSNR essentially unchanged on Shiny Blender.
  • Multi-view priors supplied without strict multi-view stereo, as in VGGT, still outperform monocular normals and depths, because consistency across views stabilizes predictions and supplies a confidence signal.

Reading between the lines

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

  • A testable extension is to apply the same confidence-weighted scheme to other multi-view estimators that produce uncertainty maps, such as pair-wise predictors in the DUSt3R/MASt3R family, to see whether the benefit is tied to VGGT specifically or to any calibrated multi-view confidence.
  • If confidence is well-calibrated, the method implies a cheap automatic masking rule: pixels with confidence near the threshold are exactly the ambiguous reflections, and a dataset-level study could correlate the confidence threshold with material specularity.
  • The approach suggests a two-stage pipeline improvement: use the reconstruction produced with confidence-weighted priors to refine the confidence predictor, closing the loop between 3DGS geometry and the prior model.
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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

4 major / 4 minor

Summary. The paper proposes a plug-in geometric regularization for 3D Gaussian splatting reconstruction: multi-view depth and normal priors predicted by VGGT are aligned to the splat geometry and added to the PGSR loss, with each prediction weighted by VGGT's confidence map. The authors report that multi-view priors outperform monocular priors and that confidence-based weighting is central to making the priors helpful, with the headline result being a reduction of Shiny Blender mean normal MAE from 3.23 degrees (PGSR) to 1.23 degrees (Ours). They also report DTU chamfer distance, TnT F1, and Shiny Blender PSNR, plus ablations on confidence weighting and prior type.

Significance. If the central claim holds, the paper makes a useful and simple contribution: it shows that the confidence maps produced by a multi-view geometry model can be used to gate geometric priors, converting an otherwise harmful regularizer into a beneficial one on specular scenes. The evaluation is not circular, since the VGGT priors and confidence maps come from a pretrained model and are not fitted to the benchmark targets, and the authors release code. The large Shiny Blender geometry gain and the honest discussion of small gains on DTU/TnT are strengths. However, the attribution of the headline gain specifically to confidence weighting is currently under-evidenced, and the paper's abstract claims a consistency that its own tables do not support.

major comments (4)
  1. [§4.6, Table 3] The manuscript's headline result is the Shiny Blender normal MAE improvement from 3.23 (PGSR) to 1.23 (Ours) in Table 1, but Table 3, the only ablation that isolates confidence-based weighting, reports only DTU and three TnT scenes. On those datasets the overall method is essentially at parity with PGSR (DTU CD 0.52 vs 0.53; TnT F1 0.50 vs 0.50 in Table 1), whereas the w/o-confidence variant is worse than the PGSR baseline on both (DTU 0.57 vs 0.53; TnT 0.37 vs 0.38 in Table 3). Consequently, the central attribution of the large specular-object gain to confidence weighting is not supported by the reported evidence; it could in principle come from the VGGT priors themselves. Please add the w/o-confidence ablation on the four Shiny Blender objects, with per-object values.
  2. [§3.1, Eq. (1) and §4.6, Table 3] Equation (1) always masks the affine alignment with M = C_i > 0.5, and the ablation section does not state that this mask is disabled in the 'w/o conf' rows of Table 3 and Supplementary Tables 1-2. If the mask remains active, the ablation removes only the C^{f(k)} weighting in Eq. (8), not the use of confidence, so the comparison does not fully isolate confidence information. Please run and report a no-confidence variant that also uses an unmasked or uniformly weighted alignment, and state explicitly which components are disabled in each ablation row.
  3. [Abstract and Table 1] The abstract and the Table 1 caption claim 'consistent improvement' and 'works best on average in all of the dataset', but Table 1 shows DTU mean CD 0.52 vs 0.53 for PGSR and TnT mean F1 0.50 vs 0.50, i.e., parity with the baseline on both datasets, and ties with GausSurf on DTU. Section 4.4 itself states that the method shows little improvement on DTU or TnT. Please either soften the global claims to 'significant gains on specular scenes with no degradation elsewhere' or provide evidence of statistically meaningful gains on DTU/TnT.
  4. [§3.2, Eqs. (8) and (10)] The method's mechanism depends on VGGT confidence being well-calibrated: the mask threshold (C_i > 0.5) and the exponent schedule f(k) are both fixed without a sensitivity analysis. Since the central claim is that confidence maps significantly improve prior integration, please report robustness of the Shiny Blender result to the confidence threshold (e.g., 0.3/0.5/0.7) and to the decay schedule, or otherwise justify the chosen values. A control experiment with a randomized or permuted confidence map would also help establish that the specific confidence values, rather than only the presence of a mask/weighting mechanism, are responsible for the improvement.
minor comments (4)
  1. [§4.1 and Table 2] Section 4.1 says the Shiny Blender subset is car, coffee, helmet, and toaster, but Table 2 reports results for five objects including teapot; please reconcile the object lists and state whether teapot is used for rendering only.
  2. [§4.2, Implementation details] Equation (10) writes f(k) = k-3000/3000 without parentheses; as written, f(3000)=0, so W_conf = C^0 = 1 at the first iteration where the prior is applied. Please clarify the intended ramp (e.g., clamp to [0,1], or define f(k) = (k-3000)/3000) and state explicitly how the prior is scheduled.
  3. [§4.6 and Supplementary Table 2] The entries '/' for +DA on Caterpillar and Ignatius are described as a failure of DA to reconstruct large scenes, but no quantitative failure criterion is given; please state what threshold or condition causes a '/' entry.
  4. [§1, Figure 2 caption] The sentence 'an issue that is in general unavoidable Figure 2 shows...' is missing punctuation before 'Figure 2', and 'smooths out' should be 'smoothes out'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the priors and confidence maps come from an external pretrained model, the geometry targets are held-out ground truth, and the final optimization is driven by photometric losses plus a fixed regularization.

full rationale

The paper's derivation chain is self-contained and does not exhibit circularity. The geometric priors (depth, normals, confidence) are outputs of the external, pretrained VGGT model [24], and are not fitted to the benchmark targets or to the final reconstruction. The regularization loss L_geo in Eq. (8) weights the discrepancy between rendered and predicted geometry by W_conf = C^{f(k)}, and the overall objective in Eq. (9) combines this with the PGSR losses L_pgsr; the reported metrics (DTU Chamfer distance, TnT F1, Shiny Blender normal MAE) are computed against held-out ground truth and are never used in the loss during optimization. Hyperparameters such as lambda_normal, lambda_depth, and f(k) are hand-selected, which is parameter tuning rather than fitting the prediction to its own target. The only self-citation by the present authors, Ref. [44], is used as background for geometry-producing operators in 3DGS and is not load-bearing for the central claim. Two experimental-completeness concerns exist but are not circularity: the ablation labeled 'w/o conf' still uses the confidence mask C_i > 0.5 for the affine alignment of depth maps in Eq. (1), so the isolation of confidence weighting is not perfectly clean, and no Shiny Blender w/o-confidence ablation is reported; however, these are limitations of the evidence, not instances where a 'prediction' reduces by construction to its input. The paper also explicitly acknowledges little improvement on DTU/TnT in Section 4.4, which is consistent with the headline gain being confined to specular scenes. No load-bearing step can be quoted that equates an output to an input by definition or by fitted feedback.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The paper contributes no new physical entities or mathematical objects. The confidence map comes from the pretrained VGGT model. The central claim rests on hand-picked hyperparameters (weights, threshold, ramp schedule) and on trust in VGGT's confidence calibration, which is not verified on the benchmark data. These are the main 'pulled' quantities.

free parameters (5)
  • lambda_normal = 0.1
    Weight for the normal regularization term in Eq. (8); no sensitivity analysis reported.
  • lambda_depth = 0.1
    Weight for the depth regularization term in Eq. (8); no sensitivity analysis reported.
  • confidence_threshold = 0.5
    Hard mask M = C_i > 0.5 used in the alignment (Section 3.1) and implicitly in weighting; chosen without calibration.
  • ramp_start_iteration = 3000
    Geometric regularization is applied only after iteration 3000, and f(k) = (k-3000)/3000 controls decay; this schedule is chosen without reported tuning.
  • decay_exponent_f(k) = (k-3000)/3000
    Scaling function for confidence weights in Eq. (8); the paper states it but does not justify the form.
assumptions (3)
  • domain assumption The estimated depth from VGGT and the rendered depth from 3DGS are related by a per-view affine transformation with the confidence mask C>0.5.
    Used in Eq. (1) to align depth priors. This is a common assumption but can fail in depth discontinuities or where the mask excludes too many pixels.
  • domain assumption VGGT's confidence map is well-calibrated, so weighting the geometric loss by C^f(k) suppresses erroneous prior gradients.
    The whole method relies on this; the paper trusts VGGT's internal uncertainty without calibrating to the target datasets.
  • domain assumption The normal prior computed from finite differences of the aligned point map (Eq. 4) is a faithful estimate of surface normals.
    This is standard but the neighborhood size (up, left, down, right) is fixed and can produce noisy normals at depth discontinuities.

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Cite this review

Pith. "Pith review of Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction." pith.science (2026). https://pith.science/paper/6V7CHPII

@misc{pith2026260806117,
  author       = {Pith},
  title        = {Pith review of: Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6V7CHPII}},
  note         = {Machine review of arXiv:2608.06117}
}
read the original abstract

3D Gaussian splatting (3DGS) has emerged as a widely-used tool for novel view synthesis, offering real-time rendering in a sparse representation. However, the method's reliance on structure-from-motion initialization and photometric optimization can lead to suboptimal geometric reconstruction, particularly for objects with high specularity. In this work, we investigate the integration of geometric priors, in the form of predicted normal and depth maps, into the 3DGS framework to improve the reconstruction quality. We analyze the effect of incorporating these priors into GS-based methods and our evaluation reveals that multi-view predictions, as they are done by the recent visual geometry grounded transformer (VGGT), outperform single-view alternatives. A major factor is the existence of a confidence map for the estimations, which comes as a by-product of multi-view models and which can significantly improve the effectiveness of priors by weighting each prediction appropriately. Extensive experiments on standard benchmarks show consistent improvement in reconstruction quality and significant gains in complex scenes including specular objects.

Figures

Figures reproduced from arXiv: 2608.06117 by the authors.

Figure 1
Figure 1. Overview. We present a framework for integrating geometric priors (nor￾mal maps, depth maps) into 3D Gaussian splatting pipelines with the goal of increasing the geometric fidelity after extracting meshes. Our analysis shows that multi-view priors, in combination with confidence maps, provide significant ad￾vantages in the presence of complex material properties, especially specularity. Abstract. 3D Gaussian splatti… view at source ↗
Figure 2
Figure 2. Monocular geometric priors often improve reconstruction quality but can [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Qualitative results on the Shiny Blender dataset. VGGT has erroneous [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Ablation study on the Shiny Blender dataset. Multi-view geometric priors [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]
Figure 5
Figure 5. Figure 5: Ablation study on confidence weighting. Ours using confidence weighting [PITH_FULL_IMAGE:figures/full_fig_p011_5.png]

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Works this paper leans on

44 extracted references · 28 canonical work pages

  1. [1]

    arXiv preprint arXiv:2512.11508 (2025)

    Bratuli´ c, J., Mittal, S., Brox, T., Rupprecht, C.: On geometric understanding and learned data priors in vggt. arXiv preprint arXiv:2512.11508 (2025)

    work page arXiv 2025

  2. [2]

    IEEE Transactions on Visualization and Computer Graphics (2024)

    Chen, D., Li, H., Ye, W., Wang, Y., Xie, W., Zhai, S., Wang, N., Liu, H., Bao, H., Zhang, G.: Pgsr: Planar-based gaussian splatting for efficient and high-fidelity sur- face reconstruction. IEEE Transactions on Visualization and Computer Graphics (2024)

    2024

  3. [3]

    Chen, G., Wang, W.: A survey on 3d gaussian splatting (2024), https://arxiv.org/ abs/2401.03890

    arXiv 2024

  4. [4]

    In: European Conference on Computer Vision (ECCV)

    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 (ECCV). pp. 370–386. Springer (2024)

    work page 2024

  5. [5]

    In: International Confer- ence on Learning Representations

    Gao, J., Chen, Z., Yang, X., Yan, Y.: Anisdf: Fused-granularity neural surfaces with anisotropic encoding for high-fidelity 3d reconstruction. In: International Confer- ence on Learning Representations. vol. 2025, pp. 61747–61768 (2025)

    work page 2025

  6. [6]

    In: Proceed- ings of the IEEE/CVF International Conference on Computer Vision

    Ge, W., Hu, T., Zhao, H., Liu, S., Chen, Y.C.: Ref-neus: Ambiguity-reduced neural implicit surface learning for multi-view reconstruction with reflection. In: Proceed- ings of the IEEE/CVF International Conference on Computer Vision. pp. 4251– 4260 (2023)

    work page 2023

  7. [7]

    ACM Transactions on Graphics (TOG)44(6), 1–15 (2025)

    Gu´ edon, A., Gomez, D., Maruani, N., Gong, B., Drettakis, G., Ovsjanikov, M.: Milo: Mesh-in-the-loop gaussian splatting for detailed and efficient surface recon- struction. ACM Transactions on Graphics (TOG)44(6), 1–15 (2025)

    work page 2025

  8. [8]

    Computer Vision and Pattern Recognition (CVPR) (2024)

    Gu´ edon, A., Lepetit, V.: Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering. Computer Vision and Pattern Recognition (CVPR) (2024)

    work page 2024

Show all 44 references
  1. [9]

    In: SIGGRAPH 2024 Conference Papers

    Huang, B., Yu, Z., Chen, A., Geiger, A., Gao, S.: 2d gaussian splatting for geometri- cally accurate radiance fields. In: SIGGRAPH 2024 Conference Papers. Association for Computing Machinery (2024). https://doi.org/10.1145/3641519.3657428

    2024

  2. [10]

    In: Proceedings of the IEEE conference on computer vision and pattern recognition

    Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aanæs, H.: Large scale multi-view stereopsis evaluation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 406–413 (2014)

    2014

  3. [11]

    ACM Transactions on Graphics42(4) (2023)

    Kerbl, B., Kopanas, G., Leimk¨ uhler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics42(4) (2023)

    2023

  4. [12]

    ACM Transactions on Graphics (ToG)36(4), 1–13 (2017)

    Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG)36(4), 1–13 (2017)

    2017

  5. [13]

    In: European Conference on Computer Vision

    Leroy, V., Cabon, Y., Revaud, J.: Grounding image matching in 3d with mast3r. In: European Conference on Computer Vision. pp. 71–91. Springer (2024)

    2024

  6. [14]

    ACM Multimedia (2025)

    Li, M., Pang, P., Fan, H., Huang, H., Yang, Y.: Tsgs: Improving gaussian splatting for transparent surface reconstruction via normal and de-lighting priors. ACM Multimedia (2025)

    2025

  7. [15]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Li, Z., M¨ uller, T., Evans, A., Taylor, R.H., Unberath, M., Liu, M.Y., Lin, C.H.: Neuralangelo: High-fidelity neural surface reconstruction. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8456–8465 (2023) 14 H. Zhou et al

    2023

  8. [16]

    Information Fusion p

    Li, Z., Yao, S., Chu, Y., Garcia-Fernandez, A.F., Yue, Y., Ding, W., Zhu, X.: Mvg-splatting: Multi-view guided gaussian splatting with adaptive quantile-based geometric consistency densification. Information Fusion p. 103540 (2025)

    2025

  9. [17]

    arXiv preprint arXiv:2511.10647 (2025)

    Lin, H., Chen, S., Liew, J., 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)

    2025 arXiv

  10. [18]

    ACM Transac- tions on Graphics (TOG)43(6), 1–12 (2024)

    Lyu, X., Sun, Y.T., Huang, Y.H., Wu, X., Yang, Z., Chen, Y., Pang, J., Qi, X.: 3dgsr: Implicit surface reconstruction with 3d gaussian splatting. ACM Transac- tions on Graphics (TOG)43(6), 1–12 (2024)

    2024

  11. [19]

    IEEE Transactions on Pattern Analysis and Machine Intelligence44(3) (2022)

    Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence44(3) (2022)

    2022

  12. [20]

    In: Computer Vision and Pattern Recognition (CVPR) (2024)

    Shao, Z., Wang, Z., Li, Z., Wang, D., Lin, X., Zhang, Y., Fan, M., Wang, Z.: Splat- tingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting. In: Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  13. [21]

    arXiv preprint arXiv:2507.06103 (2025)

    Song, J., Ye, Z., Zhou, Q., Yang, W., Fei, B., Xu, J., He, Y., Ouyang, W.: Reflec- tions unlock: Geometry-aware reflection disentanglement in 3d gaussian splatting for photorealistic scenes rendering. arXiv preprint arXiv:2507.06103 (2025)

    2025 arXiv

  14. [22]

    CVPR (2022)

    Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: Structured view-dependent appearance for neural radiance fields. CVPR (2022)

    2022

  15. [23]

    IEEE Transactions on Pattern Analysis and Machine Intelligence47(11), 9426–9437 (2024)

    Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-nerf: Structured view-dependent appearance for neural radiance fields. IEEE Transactions on Pattern Analysis and Machine Intelligence47(11), 9426–9437 (2024)

    2024

  16. [24]

    In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) (2025)

    Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., Novotny, D.: Vggt: Visual geometry grounded transformer. In: Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) (2025)

    2025

  17. [25]

    arXiv preprint arXiv:2411.19454 (2024)

    Wang, J., Liu, Y., Wang, P., Lin, C., Hou, J., Li, X., Komura, T., Wang, W.: Gaussurf: Geometry-guided 3d gaussian splatting for surface reconstruction. arXiv preprint arXiv:2411.19454 (2024)

    2024 arXiv

  18. [26]

    arXiv preprint arXiv:2106.10689 (2021)

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

    2021 arXiv

  19. [27]

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

    Wang, R., Xu, S., Dai, C., Xiang, J., Deng, Y., Tong, X., Yang, J.: Moge: Unlocking accurate monocular geometry estimation for open-domain images with optimal training supervision. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5261–...

    2025

  20. [28]

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

    Wang, S., Leroy, V., Cabon, Y., Chidlovskii, B., Revaud, J.: Dust3r: Geometric 3d vision made easy. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20697–20709 (2024)

    2024

  21. [29]

    Wang, T., Hou, Y., Zhang, Z., Xu, Y., Zhan, Z., Wang, X.: Gs-i3: Gaussian splat- ting for surface reconstruction from illumination-inconsistent images (2025)

    2025

  22. [30]

    In: European Conference on Computer Vision

    Xu, W., Gao, H., Shen, S., Peng, R., Jiao, J., Wang, R.: Mvpgs: Excavating multi- view priors for gaussian splatting from sparse input views. In: European Conference on Computer Vision. pp. 203–220. Springer (2024)

    2024

  23. [31]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Yan, C., Qu, D., Xu, D., Zhao, B., Wang, Z., Wang, D., Li, X.: Gs-slam: Dense vi- sual slam with 3d gaussian splatting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  24. [32]

    In: ECCV (2024) Leveraging Multi-view Geometric Priors for GS-based Reconstruction 15

    Yan, Y., Lin, H., Zhou, C., Wang, W., Sun, H., Zhan, K., Lang, X., Zhou, X., Peng, S.: Street gaussians for modeling dynamic urban scenes. In: ECCV (2024) Leveraging Multi-view Geometric Priors for GS-based Reconstruction 15

    2024

  25. [33]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Un- leashing the power of large-scale unlabeled data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

    2024

  26. [34]

    Advances in Neural Information Processing Systems37, 21875–21911 (2024)

    Yang, L., Kang, B., Huang, Z., Zhao, Z., Xu, X., Feng, J., Zhao, H.: Depth anything v2. Advances in Neural Information Processing Systems37, 21875–21911 (2024)

    2024

  27. [35]

    In: International Conference on Learning Representations

    Yao, Y., Zeng, Z., Gu, C., Zhu, X., Zhang, L.: Reflective gaussian splatting. In: International Conference on Learning Representations. vol. 2025, pp. 68695–68711 (2025)

    2025

  28. [36]

    Advances in neural information processing systems34, 4805–4815 (2021)

    Yariv, L., Gu, J., Kasten, Y., Lipman, Y.: Volume rendering of neural implicit surfaces. Advances in neural information processing systems34, 4805–4815 (2021)

    2021

  29. [37]

    ACM Trans- actions on Graphics (TOG)43(6), 1–18 (2024)

    Ye, C., Qiu, L., Gu, X., Zuo, Q., Wu, Y., Dong, Z., Bo, L., Xiu, Y., Han, X.: Sta- blenormal: Reducing diffusion variance for stable and sharp normal. ACM Trans- actions on Graphics (TOG)43(6), 1–18 (2024)

    2024

  30. [38]

    In: ACM SIGGRAPH 2024 Conference Papers

    Ye, K., Hou, Q., Zhou, K.: 3d gaussian splatting with deferred reflection. In: ACM SIGGRAPH 2024 Conference Papers. pp. 1–10 (2024)

    2024

  31. [39]

    arXiv preprint arXiv:2403.16964 (2024)

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

    2024 arXiv

  32. [40]

    Advances in neural information processing systems35, 25018–25032 (2022)

    Yu, Z., Peng, S., Niemeyer, M., Sattler, T., Geiger, A.: Monosdf: Exploring monoc- ular geometric cues for neural implicit surface reconstruction. Advances in neural information processing systems35, 25018–25032 (2022)

    2022

  33. [41]

    ACM Transactions on Graphics (2024)

    Yu, Z., Sattler, T., Geiger, A.: Gaussian opacity fields: Efficient and compact sur- face reconstruction in unbounded scenes. ACM Transactions on Graphics (2024)

    2024

  34. [42]

    arXiv preprint arXiv:2412.03428 (2024)

    Zhang, W., Xiang, H., Liao, Z., Lai, X., Li, X., Zeng, L.: 2dgs-room: Seed-guided 2d gaussian splatting with geometric constrains for high-fidelity indoor scene re- construction. arXiv preprint arXiv:2412.03428 (2024)

    2024 arXiv

  35. [43]

    Advances in Neural Information Processing Systems38, 89896–89917 (2026)

    Zhang, W., Tang, J., Zhang, W., Fang, Y., Liu, Y.S., Han, Z.: Materialrefgs: Re- flective gaussian splatting with multi-view consistent material inference. Advances in Neural Information Processing Systems38, 89896–89917 (2026)

    2026

  36. [44]

    Interna- tional Conference on 3D Vision (3DV) (2026) 16 H

    Zhou, H., L¨ ahner, Z.: Laplace-beltrami operator for gaussian splatting. Interna- tional Conference on 3D Vision (3DV) (2026) 16 H. Zhou et al. Confidence matters: Leveraging Multi-view Geometric Priors for GS-based Reconstruction Supplementary Material A Multi-view & monocul...

    2026

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.