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REVIEW 5 major objections 6 minor 74 references

Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read By anchoring Gaussians to an evolving signed distance field, Gaussian Sculpting reconstructs cleaner and more complete object meshes than prior NeRF- and Gaussian-based methods.

desk verdict Solid engineering contribution with best mean CD on two benchmarks, but the key gradient-isolation mechanism is only qualitatively supported and the paper lacks code, error bars, and a supplement. read the letter →

arxiv 2608.10602 v1 pith:MGIVMSGC submitted 2026-08-11 cs.CV cs.GR

Ke Jiaxin , Juncheng Liu , Yi Wang , Zhouhui Lian , Bin Liu , Shengfa Wang , Xiangjia He This is my paper · ORCID
classification cs.CVcs.GR
keywords surfacereconstruction3DGaussiansplattingsigneddistancefielddifferentiableextractionbi-leveloptimizationneuralimplicitrepresentationoctreesubdivisionmeshquality
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 claims that surface reconstruction from multi-view images can be done end-to-end by letting Gaussians sculpt a signed distance field, instead of extracting a mesh only as a post-processing step. The method anchors Gaussians to the surface mesh extracted from an evolving SDF, so the Gaussians act as geometry-aware rendering proxies whose photometric errors push the field. A bi-level optimization keeps the two roles separate: the inner loop fits Gaussian appearance to the fixed surface, and the outer loop moves the surface using the residual rendering error. On OmniObject3D the method reports a mean Chamfer distance of $9.09\times10^{-3}$, the best among the listed NeRF-based and Gaussian-based baselines, and on NeRF Synthetic it reports $1.16\times10^{-2}$ at 128 resolution, also best. If this holds, editable, watertight object meshes could be produced from limited views without manual cleanup.

What carries the argument

The central machinery is a surface-anchored Gaussian layer coupled to a neural signed distance field through a bi-level loop. Each triangular face of the extracted mesh hosts $K$ Gaussians whose centers are learnable barycentric combinations of the face vertices; each covariance is built from the face normal and edge directions with a small normal scale, so the Gaussians are flat disks lying on the surface. Three constraints keep the proxy honest: opacity is fixed near 1, the tangential scale is bounded by the face-aligned minimum enclosing ellipse via a spectral condition $\lambda_{\max}(\tilde{\Sigma}_k)\le 1$, and distribution losses penalize boundary collapse, excess clustering, and incomplete coverage of the barycentric domain. The geometry itself is an MLP-valued SDF; a Flexicubes-style differentiable extraction converts it to a mesh each iteration, the inner loop fits the Gaussians to that fixed mesh, and the outer loop backpropagates the remaining photometric error through the synchronized Gaussians into the SDF. An octree-like subdivision refines only surface-intersecting voxels, keeping memory low.

What would settle it

Freeze the SDF on a specular or textureless object, run the inner appearance loop to convergence, and measure the residual photometric loss against the distance from the extracted mesh to the ground-truth surface. If the inner loop can drive the loss to near zero while the mesh remains far from the true geometry, then rendering error after appearance adaptation is not a faithful geometry signal and the central claim fails.

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Extended reading notes

Core claim

On its own terms, the paper's central discovery is that constraining Gaussians to lie on an evolving differentiable surface converts photometric rendering error into a usable gradient for signed distance field optimization, and that this yields cleaner and more complete meshes than prior NeRF-based and Gaussian-based methods. The supporting numbers are the mean Chamfer distances: $9.09\times10^{-3}$ on OmniObject3D and $1.16\times10^{-2}$ on NeRF Synthetic at resolution 128, both best among the compared baselines. The method also removes redundant floating surfaces, recovers structures in regions with missing viewpoints, and produces meshes with fewer sliver triangles and more equilateral faces.

Load-bearing premise

The load-bearing premise is that the photometric error left after the inner appearance loop has done its best is a reliable signal for moving the surface; if appearance parameters can absorb that error without any geometry change, the outer-loop gradients will push the SDF in misleading directions.

Editorial extensions

If this is right

  • Because the mesh is the direct optimization target, downstream editing and simulation receive a watertight, topologically regular surface rather than a noisy point cloud or an opacity field.
  • The strong result at resolution 128 indicates that the method weakens the usual resolution-versus-memory trade-off for object-level reconstruction.
  • Fixing opacity near 1 prevents erroneous geometry from being hidden by transparent Gaussians, which is why floating artifacts disappear.
  • Recovering structures in missing-view regions follows from optimizing a continuous field, since observed viewpoints can indirectly supervise unobserved surface areas.

Reading between the lines

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

  • Editorial inference: the bi-level pattern is transferable: any differentiable renderer whose appearance parameters can be fit in an inner loop could be used to sculpt an SDF, not just Gaussian splatting.
  • Editorial inference: the outer-loop gradient magnitude could serve as a measure of geometric observability; regions where the inner loop absorbs all photometric error are exactly where additional views or priors are needed.
  • Editorial inference: scaling beyond object-level scenes would likely require retaining converged Gaussians instead of re-optimizing the inner loop from scratch, which the authors note as future work.
  • Editorial inference: a direct stress test for the method would be a scene with a large textureless region, where appearance can trivially explain the images and geometry has no photometric anchor.
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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

5 major / 6 minor

Summary. The paper proposes Gaussian Sculpting, an end-to-end framework for multi-view surface reconstruction that combines an MLP-based signed distance field with 3D Gaussian splatting. Gaussians are anchored to a mesh extracted from the SDF by a differentiable iso-surface extraction module, and their parameters are constrained so that the Gaussians act as geometry-aware rendering proxies. Training is formulated as a bi-level optimization: an inner loop fits Gaussian appearance on a detached copy of the scene for a fixed surface, and an outer loop uses the same photometric loss to update the SDF. The method also introduces an octree-like adaptive subdivision scheme for resolution control. Experiments on OmniObject3D and NeRF Synthetic report lower mean Chamfer distances than several NeRF-based and Gaussian-based baselines, together with qualitative and quantitative evaluations of mesh regularity and ablations of the opacity, scale, and distribution constraints.

Significance. If the empirical claims hold, the paper makes a useful contribution to Gaussian-based surface reconstruction: it introduces a clean surface-anchored Gaussian parameterization, a set of geometric constraints, and a practical bi-level optimization scheme that appears to improve both reconstruction accuracy and mesh quality. The ablation study in Table 3 supports the value of each constraint, and the mesh-quality analysis in Figure 9 is a genuinely useful addition. The explicit discussion of limitations in Section 5 is also to the authors' credit. However, the manuscript as submitted is not yet reproducible: core implementation details are deferred to a missing supplement, no code is provided, and all benchmark numbers are single-run point estimates without statistical support. The central mechanism of the method, the geometry signal in the outer-loop photometric gradient, currently rests on a qualitative comparison only, which is insufficient for a claim that the field-optimization mechanism is what drives the reported gains.

major comments (5)
  1. [Sec. 3.3, Eqs. (12)-(14)] The central geometry-learning signal can be neutralized by the inner loop's appearance adaptation. Since the inner loop freely optimizes per-Gaussian color and barycentric center weights on a detached copy, it can drive LRGB to low values on any fixed, possibly incorrect surface; the outer-loop gradient then reflects the residual after appearance fitting rather than a direct geometric error. The fixed high opacity prevents hiding errors by lowering opacity, but color and in-face position remain strong free variables. The only support for the gradient-isolation mechanism is the qualitative comparison in Fig. 7(b). Please provide a quantitative validation that the outer-loop photometric gradient is correlated with true geometric error, for example by measuring the agreement between gradient-based surface vertex updates and the direction of decreasing Chamfer distance, or by reporting a scalar-metric ablation of joint versus isolated training.
  2. [Table 1, Sec. 4.2] The reported mean Chamfer distances in Table 1 exclude failed baseline scenes without disclosure. For example, the NeuS mean of 20.21 is computed over 11 successful scenes rather than 12, and the GSDF mean of 14.68 over 9 scenes, as indicated by the '—' entries. Because Ours has no failures, the headline 'best mean' comparison is not apples-to-apples. The authors should report the number of failures per method, compute means over the common subset of scenes that all methods solve, or include a penalty for failures in the mean.
  3. [Sec. 3.2, 3.3, 4.1] The paper defers core implementation details to a missing supplement: the exact mechanics of gradient isolation in the bi-level training, the octree-like subdivision consistency rules and vertex remapping, and the precise values of K, λ_b, λ_d, λ_c, τ, δ_target, ε, θ, and λ are all referred to as supplementary material. No supplement is provided with the arXiv submission, and no code is released. As a result, the proposed method cannot be reproduced or independently checked. The authors should provide the supplement and, ideally, code with the revision.
  4. [Tables 1-3, Sec. 4.2-4.4] All quantitative results are single-run point estimates without error bars, seeds, or significance tests. Given the small differences used to support ranking claims (for example, Ours at 6.10 versus GSDF at 8.14 on Teapot in Table 1, and Ours128 at 1.16 versus 3DGSR at 1.21 in Table 2), the reported improvements could be within run-to-run variation. The authors should report variance over multiple runs or at least explicitly state that only a single run was performed and interpret the results accordingly.
  5. [Table 2, Sec. 4.3] The resolution-controllability claim is incompletely supported because Table 2 reports only Ours128 for the proposed method. Despite the introduction of a multi-resolution octree-like subdivision scheme, no high-resolution result for Ours is reported, making it impossible to assess whether the subdivision scheme preserves fine details or how the method compares at higher resolutions. The authors should add Ours-high results or an explicit resolution sweep.
minor comments (6)
  1. [Sec. 3.3, Eq. (12)] The photometric loss in Eq. (12) is the standard 3DGS loss from Kerbl et al. (2023), but it is attributed to Wang et al. (2004); the citation should be corrected.
  2. [Sec. 3.1, Eq. (11)] Setting θ→1 makes σ^{-1}(θ) unbounded; the actual finite opacity value used in the implementation should be stated explicitly.
  3. [Sec. 3.1, Eqs. (4)-(5)] The connection between λmax(Σ̃_k)≤1 and containment in the minimum enclosing ellipse is stated without proof or derivation; the cited ellipsoid-inclusion result alone does not justify the specific covariance normalization chosen.
  4. [Sec. 4.1] The text says mesh quality is statistically analyzed using maximum angle, minimum angle, radius ratio, aspect ratio, and sliver percentage, but Figure 9 reports only angle distributions; the remaining metrics should be reported in a table or stated to be in the supplement.
  5. [Sec. 4.2] The claim that SuGar and 2DGS perform poorly on OmniObject3D 'due to their suboptimal handling of datasets with a single object and masked backgrounds' is an unsupported attribution to the baselines' implementation; please substantiate this with evidence or soften the wording.
  6. [Sec. 2.3] The Marching Tetrahedra reference is cited as Lorensen and Cline 1998, which is the Marching Cubes paper; a proper citation for Marching Tetrahedra should be used.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: benchmark metrics are external and the bi-level optimization is an algorithmic choice, not a definitional reduction.

full rationale

The paper's central quantitative claims are Chamfer-distance comparisons on NeRF Synthetic and OmniObject3D against external ground-truth meshes, computed with evaluation scripts taken from GOF and FlexiCubes; no parameter is fitted to those targets and then reported as a prediction. The bi-level scheme in Section 3.3 (Eqs. 13-14) is the closest candidate: the inner loop adapts a detached Gaussian scene on a fixed surface, and the outer loop re-evaluates the same photometric loss to update the SDF. This could in principle be an ineffective or weakly informative geometry signal if the inner-loop appearance parameters absorb all rendering error, and the paper supports the gradient-isolation claim only qualitatively in Fig. 7(b). However, that is an empirical effectiveness and reproducibility concern, not circularity: the geometry is not defined in terms of the predicted mesh, no fitted constant is renamed as a prediction, and the evaluated metric is not an input to the optimization. The opacity, scale, and distribution constraints are design choices that strengthen the coupling between rendering and geometry rather than tautological constructions. No load-bearing self-citations appear; references such as FlexiCubes, GOF, NeuS, and 2DGS are external prior work, not a self-citation chain that forces the result. Missing implementation details and a qualitative-only ablation affect verifiability and correctness risk, but they do not make the derivation equivalent to its own inputs.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central empirical result depends on a number of unreported hyperparameters and on several domain assumptions inherited from differentiable extraction and 3DGS. No new physical entities are introduced; the detached Gaussian copy is a training mechanism, not an invented entity.

free parameters (7)
  • K (Gaussians per face)
    Number of Gaussians instantiated on each mesh face; never specified, yet it sets expressiveness and appears in the distribution losses (Eqs. 6-8).
  • lambda_b, lambda_d, lambda_c
    Balancing weights for boundary, distance, and coverage losses in Eq. 10; values not reported.
  • tau (coverage threshold) = 0.8
    Hand-chosen threshold in coverage loss L_c (Eq. 9).
  • delta_target = 1/sqrt(K)
    Target Gaussian spacing, described as 'empirically chosen' in Eq. 7.
  • epsilon (normal scale)
    Small scale s0 used to flatten Gaussians onto faces; value not given in the main text.
  • theta (opacity target) = close to 1
    Constant opacity fixed via inverse sigmoid in Eq. 11; exact value not reported.
  • lambda in LRGB
    Blend weight between L1 and D-SSIM in Eq. 12; not specified.
assumptions (5)
  • domain assumption FlexiCubes differentiable iso-surface extraction correctly propagates gradients from mesh vertices to SDF values
    Used in Section 3.1 as the differentiable extraction module; correctness is taken from Shen et al. 2023 without re-derivation.
  • ad hoc to paper Fixed-opacity surface-anchored Gaussians provide a faithful rendering proxy whose photometric error is attributable to geometry
    Core premise of Sections 3.1-3.3; if color can absorb photometric error, SDF gradients are misled.
  • ad hoc to paper Bi-level gradient isolation stabilizes joint optimization
    Section 3.3; only qualitative evidence in Fig. 7, no theoretical justification.
  • ad hoc to paper MLP SDF continuity enables indirect supervision of unobserved regions from other viewpoints
    Invoked in Section 4.2 to explain completeness; no explicit multi-view consistency constraint is introduced.
  • domain assumption Adjacent voxels subdivided consistently preserve dual-vertex computation
    Section 3.2; required for octree-like adaptive subdivision to remain watertight.

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Cite this review

Pith. "Pith review of Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization." pith.science (2026). https://pith.science/paper/MGIVMSGC

@misc{pith2026260810602,
  author       = {Pith},
  title        = {Pith review of: Gaussian Sculpting: End-to-End Controllable Surface Reconstruction via Field Optimization},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/MGIVMSGC}},
  note         = {Machine review of arXiv:2608.10602}
}
read the original abstract

3D Gaussian Splatting (3DGS) has recently enabled real-time novel view synthesis with impressive quality. However, it struggles to recover accurate surfaces under limited viewpoints and due to the inherent irregularity of Gaussian primitives. The resulting geometric errors are notoriously difficult to correct manually. To address these issues, we propose Gaussian Sculpting, a fully differentiable end-to-end framework for high-quality surface reconstruction. Our key insight is to anchor Gaussians onto an evolving differentiable surface, allowing them to guide signed distance field (SDF) optimization instead of extracting the surface only during post-processing. To enable stable gradient isolation during joint optimization, we design a bi-level training strategy in which the outer loop optimizes the geometry represented by the SDF, while the inner loop updates the Gaussians with the geometry fixed. We further impose constraints on Gaussian parameters to ensure consistency with the underlying surface, thereby improving both geometric and appearance fidelity during optimization. In addition, we introduce a multi-resolution subdivision scheme based on octree-like partitioning to preserve fine details while reducing memory consumption. Experiments on object-level scenes demonstrate that our method effectively removes redundant surfaces, recovers missing structures caused by limited viewpoints, and achieves strong reconstruction quality even at relatively low resolutions.

Figures

Figures reproduced from arXiv: 2608.10602 by the authors.

Figure 1
Figure 1. Gaussian Sculpting is an end-to-end surface reconstruction framework based on field optimization that progressively refines mesh geometry to recover [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Pipeline of Gaussian Sculpting. In each iteration of mesh optimization, the SDF network predicts SDF values at voxel vertices, from which an [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Use an adaptive grid refinement method to control resolution. Left: [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Mesh quality. We show magnified surface details for each surface in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 7
Figure 7. Figure 7: (a) Neural implicit SDF yields smoother and more continuous sur [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 6
Figure 6. Figure 6: Comparison of the effects of the scale constraint (left) and distri [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 8
Figure 8. Figure 8: Real scanned results on OmniObject3D. Interior (blue) and viewpoint [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: Quantitative comparison of the intrinsic quality of extracted meshes. Our method yields the lowest number of sliver triangles with interior angles [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

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Reference graph

Works this paper leans on

74 extracted references · 44 canonical work pages

  1. [1]

    Communications of the ACM , volume=

    Nerf: Representing scenes as neural radiance fields for view synthesis , author=. Communications of the ACM , volume=. 2021 , publisher=

    2021

  2. [2]

    , author=

    3D Gaussian splatting for real-time radiance field rendering. , author=. ACM Trans. Graph. , volume=

  3. [3]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Sugar: Surface-aligned gaussian splatting for efficient 3d mesh reconstruction and high-quality mesh rendering , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    work page

  4. [4]

    European conference on computer vision , pages=

    Using multiple hypotheses to improve depth-maps for multi-view stereo , author=. European conference on computer vision , pages=. 2008 , organization=

    work page 2008

  5. [5]

    International journal of computer vision , volume=

    A theory of shape by space carving , author=. International journal of computer vision , volume=. 2000 , publisher=

    2000

  6. [6]

    Seminal graphics: pioneering efforts that shaped the field , pages=

    Marching cubes: A high resolution 3D surface construction algorithm , author=. Seminal graphics: pioneering efforts that shaped the field , pages=

  7. [7]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Deep Marching Cubes: Learning Explicit Surface Representations , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    work page

  8. [8]

    Proceedings of the 29th annual conference on Computer graphics and interactive techniques , pages=

    Dual contouring of hermite data , author=. Proceedings of the 29th annual conference on Computer graphics and interactive techniques , pages=

Show all 74 references
  1. [9]

    IEEE visualization 2004 , pages=

    Dual marching cubes , author=. IEEE visualization 2004 , pages=. 2004 , organization=

    2004

  2. [10]

    Proceedings of the fourth Eurographics symposium on Geometry processing , volume=

    Poisson surface reconstruction , author=. Proceedings of the fourth Eurographics symposium on Geometry processing , volume=

  3. [11]

    International conference image analysis and recognition , pages=

    Truncated signed distance function: experiments on voxel size , author=. International conference image analysis and recognition , pages=. 2014 , organization=

    2014

  4. [12]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Occupancy Networks: Learning 3D Reconstruction in Function Space , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  5. [13]

    European conference on computer vision , pages=

    3d-r2n2: A unified approach for single and multi-view 3d object reconstruction , author=. European conference on computer vision , pages=. 2016 , organization=

    2016

  6. [14]

    Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages=

    Pix2Vox: Context-Aware 3D Reconstruction from Single and Multi-View Images , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages=

  7. [15]

    Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pages=

    A point set generation network for 3d object reconstruction from a single image , author=. Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR) , pages=

  8. [16]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  9. [17]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  10. [18]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Neural lumigraph rendering , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  11. [19]

    Advances in Neural Information Processing Systems , volume=

    Multiview neural surface reconstruction by disentangling geometry and appearance , author=. Advances in Neural Information Processing Systems , volume=

  12. [20]

    Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) , pages=

    Point-nerf: Point-based neural radiance fields , author=. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) , pages=

  13. [21]

    IEEE Transactions on Visualization and Computer Graphics , volume=

    Vox-surf: Voxel-based implicit surface representation , author=. IEEE Transactions on Visualization and Computer Graphics , volume=. 2022 , publisher=

    2022

  14. [22]

    NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction , url =

    Wang, Peng and Liu, Lingjie and Liu, Yuan and Theobalt, Christian and Komura, Taku and Wang, Wenping , booktitle =. NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction , url =

  15. [23]

    Proceedings of the IEEE/CVF international conference on computer vision , pages=

    Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction , author=. Proceedings of the IEEE/CVF international conference on computer vision , pages=

  16. [24]

    Advances in neural information processing systems , volume=

    Volume rendering of neural implicit surfaces , author=. Advances in neural information processing systems , volume=

  17. [25]

    Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages=

    NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-View Reconstruction , author=. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages=

  18. [26]

    ACM SIGGRAPH 2023 conference proceedings , pages=

    Bakedsdf: Meshing neural sdfs for real-time view synthesis , author=. ACM SIGGRAPH 2023 conference proceedings , pages=

    2023

  19. [27]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Neuralangelo: High-Fidelity Neural Surface Reconstruction , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  20. [28]

    ACM SIGGRAPH 2024 conference papers , pages=

    2d gaussian splatting for geometrically accurate radiance fields , author=. ACM SIGGRAPH 2024 conference papers , pages=

    2024

  21. [29]

    ACM Trans

    Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes , author=. ACM Trans. Graph. , volume=

  22. [30]

    IEEE Transactions on Visualization and Computer Graphics , year=

    Pgsr: Planar-based gaussian splatting for efficient and high-fidelity surface reconstruction , author=. IEEE Transactions on Visualization and Computer Graphics , year=

  23. [31]

    Advances in Neural Information Processing Systems , volume=

    Diffgs: Functional gaussian splatting diffusion , author=. Advances in Neural Information Processing Systems , volume=

  24. [32]

    arXiv preprint arXiv:2411.19454 , year=

    GausSurf: Geometry-Guided 3D Gaussian Splatting for Surface Reconstruction , author=. arXiv preprint arXiv:2411.19454 , year=

    arXiv

  25. [33]

    Advances in Neural Information Processing Systems , volume=

    Gsdf: 3dgs meets sdf for improved neural rendering and reconstruction , author=. Advances in Neural Information Processing Systems , volume=

  26. [34]

    Proceedings of the Asian Conference on Computer Vision , pages=

    Meshgs: Adaptive mesh-aligned gaussian splatting for high-quality rendering , author=. Proceedings of the Asian Conference on Computer Vision , pages=

  27. [35]

    arXiv preprint arXiv:2402.01459 , year=

    Games: Mesh-based adapting and modification of gaussian splatting , author=. arXiv preprint arXiv:2402.01459 , year=

    arXiv

  28. [36]

    ACM Trans

    Flexible Isosurface Extraction for Gradient-Based Mesh Optimization , author=. ACM Trans. Graph. , volume=

  29. [37]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Omniobject3d: Large-vocabulary 3d object dataset for realistic perception, reconstruction and generation , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  30. [38]

    ACM SIGGRAPH 2011 Talks , pages=

    Kinectfusion: real-time dynamic 3d surface reconstruction and interaction , author=. ACM SIGGRAPH 2011 Talks , pages=

    2011

  31. [39]

    Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis , url =

    Shen, Tianchang and Gao, Jun and Yin, Kangxue and Liu, Ming-Yu and Fidler, Sanja , booktitle =. Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis , url =

  32. [40]

    ArXiv , year=

    DiMeR: Disentangled Mesh Reconstruction Model , author=. ArXiv , year=

  33. [41]

    Learning Generalizable Shape Completion with SIM(3) Equivariance , url =

    Wang, Yuqing and Chen, Zhaiyu and Zhu, Xiaoxiang , booktitle =. Learning Generalizable Shape Completion with SIM(3) Equivariance , url =

  34. [42]

    ACM Trans

    3DGSR: Implicit Surface Reconstruction with 3D Gaussian Splatting , author=. ACM Trans. Graph. , volume=

  35. [43]

    ACM Trans

    GS-ROR2: Bidirectional-Guided 3DGS and SDF for Reflective Object Relighting and Reconstruction , author=. ACM Trans. Graph. , volume=

  36. [44]

    Proceedings of the 27th annual conference on Computer graphics and interactive techniques , pages=

    Adaptively sampled distance fields: A general representation of shape for computer graphics , author=. Proceedings of the 27th annual conference on Computer graphics and interactive techniques , pages=

  37. [45]

    Journal of Optimization Theory and Applications , volume=

    Computing minimum-volume enclosing axis-aligned ellipsoids , author=. Journal of Optimization Theory and Applications , volume=. 2008 , publisher=

    2008

  38. [46]

    European Conference on Computer Vision , pages=

    Relightable 3d gaussians: Realistic point cloud relighting with brdf decomposition and ray tracing , author=. European Conference on Computer Vision , pages=. 2024 , organization=

    2024

  39. [47]

    IEEE Transactions on Visualization and Computer Graphics , volume=

    Fov-nerf: Foveated neural radiance fields for virtual reality , author=. IEEE Transactions on Visualization and Computer Graphics , volume=. 2022 , publisher=

    2022

  40. [48]

    2021 IEEE Virtual Reality and 3D User Interfaces (VR) , pages=

    SuperPlane: 3D plane detection and description from a single image , author=. 2021 IEEE Virtual Reality and 3D User Interfaces (VR) , pages=. 2021 , organization=

    2021

  41. [49]

    Arcargo: Multi-device integrated cargo loading management system with augmented reality , author=. 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cybe...

    2021

  42. [50]

    arXiv preprint arXiv:2510.11878 , year=

    GS-Verse: Mesh-based Gaussian Splatting for Physics-aware Interaction in Virtual Reality , author=. arXiv preprint arXiv:2510.11878 , year=

  43. [51]

    Proceedings of the SIGGRAPH Asia 2025 Emerging Technologies , pages=

    Play4D: Accelerated and Interactive Free-viewpoint Video Streaming for Virtual Reality and Light Field Displays , author=. Proceedings of the SIGGRAPH Asia 2025 Emerging Technologies , pages=

    2025

  44. [52]

    IEEE/ASME Transactions on Mechatronics , year=

    Russo: Robust underwater slam with sonar optimization against visual degradation , author=. IEEE/ASME Transactions on Mechatronics , year=

  45. [53]

    2025 IEEE International Conference on Robotics and Automation (ICRA) , pages=

    NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection , author=. 2025 IEEE International Conference on Robotics and Automation (ICRA) , pages=. 2025 , organization=

    2025

  46. [54]

    The Thirteenth International Conference on Learning Representations , year=

    MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers , author=. The Thirteenth International Conference on Learning Representations , year=

  47. [55]

    DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation , url =

    Tang, Jiaxiang and Ren, Jiawei and Zhou, Hang and Liu, Ziwei and Zeng, Gang , booktitle =. DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation , url =

  48. [56]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Matrix3D: Large Photogrammetry Model All-in-One , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  49. [57]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    CADDreamer: CAD Object Generation from Single-View Images , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  50. [58]

    2013 , publisher=

    Delaunay mesh generation , author=. 2013 , publisher=

    2013

  51. [59]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

    Mani-GS: Gaussian Splatting Manipulation with Triangular Mesh , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages=

  52. [60]

    and Edelsbrunner, Herbert and Facello, Michael A

    Cheng, Siu-Wing and Dey, Tamal K. and Edelsbrunner, Herbert and Facello, Michael A. and Teng, Shang-Hua , title =. J. ACM , month = sep, pages =. 2000 , issue_date =. doi:10.1145/355483.355487 , abstract =

  53. [61]

    Accurate, Dense, and Robust Multiview Stereopsis , year=

    Furukawa, Yasutaka and Ponce, Jean , journal=. Accurate, Dense, and Robust Multiview Stereopsis , year=

  54. [62]

    Neural Implicit Representations for Multi-View Surface Reconstruction: A Survey , year=

    Zhang, Xinyun and Yu, Ruiqi and Ren, Shuang , journal=. Neural Implicit Representations for Multi-View Surface Reconstruction: A Survey , year=

  55. [63]

    Wang, Zhou and Bovik, A. C. and Sheikh, H. R. and Simoncelli, E. P. , title =. Trans. Img. Proc. , month = apr, pages =. 2004 , issue_date =. doi:10.1109/TIP.2003.819861 , abstract =

    2004

  56. [64]

    Egidio and Raphaël M

    Julien Calbert and Lucas N. Egidio and Raphaël M. Jungers , keywords =. An Efficient Method to Verify the Inclusion of Ellipsoids , journal =. 2023 , note =. doi:https://doi.org/10.1016/j.ifacol.2023.10.1088 , url =

    2023 doi

  57. [65]

    British Machine Vision Conference , year=

    PatchMatch Stereo - Stereo Matching with Slanted Support Windows , author=. British Machine Vision Conference , year=

  58. [66]

    Michael Downes and Barbara Beeton , organization =. The

  59. [67]

    Cristophe Fiorio , year = 2015, month = oct, note =

    2015

  60. [68]

    The algorithms bundle , author =

  61. [69]

    The Listings Package , author =

  62. [70]

    Publication quality tables in

    Simon Fear , year = 2005, month = apr, note =. Publication quality tables in

    2005

  63. [71]

    The subcaption package , author =

  64. [72]

    A package to create a nomenclature , author =

  65. [73]

    User Manual for glossaries.sty v4.44 , author =

  66. [74]

    David Carlisle , month = oct, year = 2004, note =. The

    2004

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.