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REVIEW 4 major objections 5 minor 1 cited by

NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read NumGrad-Pull's central claim is that finite-difference numerical gradients, not analytical ones, make tri-plane-based signed-distance learning stable enough to reconstruct accurate surfaces from unoriented point clouds.

desk verdict A solid, incremental tri-plane pulling system with real benchmark gains, but the central numerical-gradient claim rests on an under-specified ablation that needs pinning down before I'd trust it. read the letter →

arxiv 2411.17392 v3 pith:LZIKKONF submitted 2024-11-26 cs.CV

Ruikai Cui , Binzhu Xie , Shi Qiu , Jiawei Liu , Saeed Anwar , Nick Barnes This is my paper · ORCID
classification cs.CV
keywords signeddistancefunctionsurfacereconstructionpointcloudstri-planerepresentationnumericalgradientsneuralpullingprogressiveresolutionexpansionunoriented
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

NumGrad-Pull aims to reconstruct a clean continuous surface from a bare 3D point cloud, with no point normals, by learning a signed distance function whose zero set is the surface. The paper's central claim is that a tri-plane representation—three orthogonal feature grids with a small neural decoder—can learn such a function faster and with finer detail than deep networks alone, provided the training signal is changed. Instead of back-propagating through the analytical gradient of the network, the method estimates gradients by finite differences, which spreads each update across neighboring grid cells and stabilizes training. It adds a coarse-to-fine tri-plane expansion and a sampling mix that guides both surface-near and distant regions. On ShapeNet, ABC, FAMOUS, and real scans, the authors report lower chamfer distances (a surface error measure) than prior pulling-based methods while running several times faster.

What carries the argument

The load-bearing object is the tri-plane feature grid (three axis-aligned $N\times N$ feature planes with bilinear interpolation, summed and decoded by a shallow MLP into a signed distance), and the load-bearing mechanism is the finite-difference gradient estimator used inside the pulling update. The mechanism's job is to replace the analytically computed gradient, which through the rounding operations of interpolation touches only the four corner features of one grid cell, with a computation that evaluates the SDF at six neighboring points and thereby back-propagates to multiple grid regions at once; this is what the paper credits for training stability.

What would settle it

Reproduce the paper's Model C ablation on FAMOUS: the same tri-plane pipeline with numerical gradients swapped back to analytical gradients, keeping all other modules fixed; the paper reports a collapse to CD 331.84 versus 0.39 ($\times10^{-4}$). If that run trains stably, or fails for a different reason such as merely missing progressive expansion, the claim that numerical gradients are the stabilizing mechanism would be falsified. A complementary check on synthetic clouds with deliberately shifted nearest-neighbor targets would test the load-bearing pulling assumption directly.

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Extended reading notes

Core claim

The paper's central discovery is that the instability of tri-plane SDF training is caused by the locality of analytical gradients: bilinear interpolation with rounding means back-propagation touches only the four corner features of one grid cell. Replacing the analytical gradient in the pulling update with a central finite difference, $\nabla_x\Phi(q) = [\Phi(q+\epsilon_x)-\Phi(q-\epsilon_x)]/(2\epsilon)$ and similarly for the other axes, makes each gradient computation involve six nearby evaluations, so supervision reaches adjacent grid entities simultaneously. With that change, plus progressive resolution expansion and a complementary uniform-cube sampling strategy, the tri-plane signed distance function converges stably and produces reconstructions that the paper reports as more accurate than previous methods.

Load-bearing premise

The method's pulling targets are chosen as the nearest input point to each query, so the whole pipeline assumes that nearest neighbor in the sampled cloud is a trustworthy stand-in for the true closest point on the surface; on sparse, noisy, or incomplete scans that proxy can be wrong and drag the learned field to a biased surface.

Editorial extensions

If this is right

  • The reported FAMOUS chamfer distance drops from 11.35e-4 (baseline Neural-Pull) to 0.39e-4, meaning fine surface detail such as the hand's fingers is preserved rather than smoothed away.
  • Because finite-difference gradients couple neighboring grid cells, training converges without collapsing even when the tri-plane starts at 8x8 and expands through 16x16 to 32x32; the authors claim this is what makes the progressive expansion viable.
  • The same architecture at 32x32 runs roughly 1.8x faster than Neural-Pull and 5.4x faster than IF in iterations per second, a direct consequence of the O(1) tri-plane interpolation.
  • Each proposed component is necessary in the authors' ablation: removing numerical gradients is the most destructive (Model C, CD 331.84e-4), and removing progressive expansion or complementary sampling also degrades results.
  • On ShapeNet, the paper reports an average chamfer distance of 0.020 versus 0.032 for the strongest prior method, with the best result in six of eight categories.

Reading between the lines

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

  • If the locality diagnosis is right, the same finite-difference trick should transfer to other grid-based implicit representations such as hash grids or octrees, where analytical gradients are also local; testing NumGrad-style gradients in those settings is a natural extension the paper does not make.
  • The numerical gradient couples three extra query evaluations, so the reported speed advantage is partly offset by more forward passes; a fairer efficiency comparison would hold total network evaluations constant, not just iterations per second.
  • The nearest-neighbor pulling target remains the weak link: on noisy scans, correcting targets (e.g., via local plane fitting) before pulling could combine with numerical gradients to improve robustness, an option the paper does not explore.
  • The method's robustness claims are demonstrated on object-level scans; applying it to scene-level data with larger scale and varying density is the paper's stated future work and would test whether tri-plane resolution expansion generalizes.
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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 / 5 minor

Summary. NumGrad-Pull proposes a hybrid tri-plane/MLP signed distance function for surface reconstruction from unoriented point clouds, trained with a pulling loss. The three claimed contributions are (i) replacing analytical gradients with finite-difference numerical gradients in the pulling operation to stabilize tri-plane training, (ii) a progressive tri-plane resolution expansion schedule, and (iii) a complementary sampling strategy that augments near-surface query points with points from the unit cube. The method is evaluated on ShapeNet, ABC, FAMOUS, and the SRB scans, reporting Chamfer and Hausdorff distances that beat or match prior work (e.g., ShapeNet average CD 0.020 vs. 0.032 for IF), with faster per-iteration training. Ablations on FAMOUS show that removing numerical gradients leads to a catastrophic CD of 331.84 (scaled by 10^4) versus 0.39 for the full model, which the paper interprets as evidence for the central claim.

Significance. If the central claim is valid, the paper would show that a simple and cheap modification (finite-difference gradients) resolves a real training instability in grid-based SDF learning, which is a practically important problem. The paper ships code and evaluates on several standard benchmarks, which is a clear strength. However, the evidence for the central claim currently rests on a single ablation table with an unspecified analytical-gradient baseline, so the significance of the numerical-gradient contribution is not yet established. The progressive expansion and complementary sampling appear to be useful engineering additions, and the speed gains are credible, but these are secondary to the main claim. The overall approach is plausible and the benchmark results are encouraging, but the load-bearing evidence needs to be made rigorous before the paper's conclusions can be accepted.

major comments (4)
  1. [§3.3 and Eq. (4)-(6)] The justification for numerical gradients is based on an incorrect characterization of bilinear interpolation. Equation (4) is a continuous, piecewise-linear function of the query coordinate q; the floor and ceil indices are constant within each grid cell, and the interpolation coefficients are linear in the fractional parts. The function is differentiable almost everywhere, and the standard analytical gradient of the tri-plane feature with respect to q is well defined. The paper's statement that "the gradient of the tri-plane encoding is inherently local" because of "non-differentiable rounding operations" conflates non-differentiability at grid boundaries with the well-defined derivative inside each cell. This matters because it is the stated motivation for the central contribution. The authors should either provide a precise derivation of what the analytical gradient is and why it is problematic, or correct the explanation.
  2. [Table 5 and §4.4.2] The ablation study that carries the central claim (Model C, 'w/o NumGrad', CD 331.84 vs. 0.39 for the full model) never specifies how the analytical gradient is computed in Model C. The text only says that analytical gradients 'only propagate supervision to local grids'. If the analytical-gradient baseline is implemented by detaching the floor/ceil indices or by taking the gradient of the interpolated feature with respect to the parameters only for the four corners in the current cell, then the comparison is not a clean test of numerical versus analytical gradients; it is a test of a particular—and possibly degenerate—implementation. The manuscript must state precisely the computation used for Model C, provide the relevant code or pseudocode, and verify that the catastrophic failure is not an artifact of that implementation (e.g., a zero or arbitrarily directed gradient). Without this, the headline ablation does not support the paper's central claim.
  3. [§3.4 and Eq. (6)] The perturbation size is set to epsilon = 1/(2R), where R is the current tri-plane resolution, but no sensitivity analysis is reported. Since the numerical gradient in Eq. (6) is the mechanism supposed to stabilize training, the paper should show results for at least two alternative epsilon values (e.g., epsilon = 1/R and epsilon = 1/(4R)) and for a fixed epsilon while the resolution expands, to establish that the choice is not fine-tuned for the reported ablations. Additionally, the claim that numerical gradients improve over analytical gradients should be supported by training curves (loss or CD over iterations) rather than only a single endpoint metric.
  4. [Table 5 checkmark encoding] The ablation table is internally inconsistent with the text. The text defines Model A as 'full model without our data sampling strategy,' but the row for Model A in Table 5 shows a ✗ under 'w/o Data', implying data sampling is present, and a ✓ under 'w/o Tri-plane', 'w/o NumGrad', and 'w/o Progressive', which cannot be correct for a 'full model without one component'. The checkmark encoding is either reversed or misaligned. This must be fixed, because the table is the primary quantitative evidence for the contribution of each module. All ablation numbers are also single-run; the central comparison (0.39 vs. 331.84) would be more convincing if reported over repeated runs with variance.
minor comments (5)
  1. [§3.5] The complementary sampling strategy is described as 'randomly sampling points in a unit cube [−1,1]^3', but the number of such points per training step or per surface point is not specified. This is needed for reproducibility.
  2. [Table 3] The row for 'Ours' in Table 3 appears as a run-together string of numbers in the provided version (e.g., '0.0511.1940.0410.113...'), making the results unreadable. Please ensure the table formatting is correct in the camera-ready version.
  3. [§2.3] There is a typo: 'exploys' should be 'employs'.
  4. [§4.4.1 and Table 4] The speed comparison reports 'iter/s' without specifying how many forward/backward passes each iteration comprises. Since the numerical-gradient variant performs six additional forward evaluations per query, a per-iteration comparison overstates the practical speed advantage. Clarify this in the text.
  5. [§2.3] The sentence 'we are pioneering the exploration of hybrid explicit–implicit representations [5] for fundamental surface reconstruction problems' overstates novelty, as several tri-plane or grid-based SDF works exist (e.g., Grid-Pull [8] and Neuralangelo [23]). Please temper the claim and cite the relevant prior art.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper's claims are supported by external benchmark comparisons and ablations, not by fitting or self-referential definitions.

full rationale

NumGrad-Pull's central claims (tri-plane acceleration, numerical-gradient stability, progressive expansion, complementary sampling) are validated against external benchmarks (ShapeNet, ABC, FAMOUS, SRB) with standard protocols and compared against independently developed baselines (Neural-Pull, Grid-Pull, DIGS, IF, etc.). The pulling objective in Eq. 1 and loss in Eq. 7 are inherited self-supervised training objectives from Neural-Pull; they use the input point cloud as the supervision target, so the reconstructed surface is not defined in terms of the prediction. The numerical gradient in Eq. 6 is a finite-difference estimate of the same SDF, not a fitted parameter later reported as a prediction; the ablation in Table 5 compares training with analytical versus numerical gradients, and although the analytical-gradient baseline's implementation is underspecified (a reproducibility concern, not a circularity), the comparison is not forced by construction. The authors' prior tri-plane works are cited for context but are not the load-bearing evidence: the main improvements are measured against external methods on held-out data. The paper also explicitly discloses limitations (high-resolution noise, robustness to noisy/sparse clouds, scene-level extension) in Sec. 5, consistent with an empirical, non-circular derivation chain.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The method is an empirical pipeline. The main free parameters are the tri-plane resolution, feature channels, expansion schedule, and epsilon schedule. The axioms are standard domain assumptions inherited from Neural-Pull plus the expressiveness assumption for tri-planes. No new entities are introduced.

free parameters (6)
  • Final tri-plane resolution N = 32
    Selected by ablation on FAMOUS (Table 4); used for all main experiments. Higher resolution (64) introduces high-frequency noise.
  • Tri-plane feature channels C = 32
    Chosen in implementation details (Sec 4.1); no ablation reported.
  • Expansion iteration schedule = 3k, 8k, 12k
    Three stages chosen by hand in Sec 4.1; affects convergence and final quality.
  • Perturbation epsilon = 1/(2R)
    Tied to current tri-plane resolution (Sec 3.4); determines the stencil for numerical gradients. This is a hand-chosen schedule.
  • Query samples per point and sigma = 25 samples; sigma as 50th nearest neighbor distance
    Sampling strategy in Sec 3.5; sigma is data-adaptive but the choice of 25 and 50th neighbor is a design decision.
  • Learning rates = 0.001 MLP, 0.05 tri-plane
    Adam hyperparameters from Sec 4.1; not justified beyond standard practice.
assumptions (4)
  • domain assumption The nearest point in the input point cloud is a reliable proxy for the closest point on the true surface, so it can serve as the pulling target t_i.
    Used in the pull loss Eq. 7 (Sec 3.6). Under sparse or noisy point clouds this assumption degrades, and the method's robustness claims depend on it.
  • domain assumption The gradient of the learned SDF, normalized, points in the direction of the nearest surface point.
    Inherited from Neural-Pull; stated in Sec 3.1 and used in Eq. 1 for the pulling operation.
  • domain assumption Bilinear interpolation of tri-plane features followed by a shallow MLP is expressive enough to represent signed distance functions accurately.
    Central modelling choice of Sec 3.2; not proven, but supported by experiments.
  • standard math The Adam optimizer converges to a good local minimum for this non-convex objective.
    Standard optimization assumption used throughout training (Sec 4.1).

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Cite this review

Pith. "Pith review of NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds." pith.science (2026). https://pith.science/paper/LZIKKONF

@misc{pith2026241117392,
  author       = {Pith},
  title        = {Pith review of: NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LZIKKONF}},
  note         = {Machine review of arXiv:2411.17392}
}
read the original abstract

Reconstructing continuous surfaces from unoriented and unordered 3D points is a fundamental challenge in computer vision and graphics. Recent advancements address this problem by training neural signed distance functions to pull 3D location queries to their closest points on a surface, following the predicted signed distances and the analytical gradients computed by the network. In this paper, we introduce NumGrad-Pull, leveraging the representation capability of tri-plane structures to accelerate the learning of signed distance functions and enhance the fidelity of local details in surface reconstruction. To further improve the training stability of grid-based tri-planes, we propose to exploit numerical gradients, replacing conventional analytical computations. Additionally, we present a progressive plane expansion strategy to facilitate faster signed distance function convergence and design a data sampling strategy to mitigate reconstruction artifacts. These components are synergistically integrated into a unified tri-plane-based pulling framework, in which numerical gradients, progressive expansion, and complementary sampling jointly address the locality and sparsity challenges of learning SDFs from unoriented point clouds. Our extensive experiments across a variety of benchmarks demonstrate the effectiveness and robustness of our approach. Codes are available at: https://github.com/cuiruikai/numgrad-pull.

Figures

Figures reproduced from arXiv: 2411.17392 by the authors.

Figure 1
Figure 1. We present NumGrad-Pull, a tri-plane-based framework [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of our method. (a) The NumGrad-Pull framework parameterizes a neural signed distance function using a hybrid [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Visual comparisons of surface reconstruction quality on the synthetic datasets (ABC [ [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Visual results of surface reconstruction using different [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Visual results of using different model variants. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Forward citations

Cited by 1 Pith paper

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

  1. NoiseSDF2NoiseSDF: Learning Clean Neural Fields from Noisy Supervision

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Reference graph

Works this paper leans on

40 extracted references · 33 canonical work pages · cited by 1 Pith paper

  1. [1]

    Surface reconstruction by voronoi filtering

    Nina Amenta and Marshall Bern. Surface reconstruction by voronoi filtering. In Proceedings of the fourteenth annual symposium on Computational geometry, pages 39–48, 1998. 2

    work page 1998

  2. [2]

    Digs: Divergence guided shape implicit neu- ral representation for unoriented point clouds

    Yizhak Ben-Shabat, Chamin Hewa Koneputugodage, and Stephen Gould. Digs: Divergence guided shape implicit neu- ral representation for unoriented point clouds. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19323–19332, 2022. 1, 5, 6

    work page 2022

  3. [3]

    Geometric structures for three- dimensional shape representation

    Jean-Daniel Boissonnat. Geometric structures for three- dimensional shape representation. ACM Transactions on Graphics (TOG), 3(4):266–286, 1984. 2

    work page 1984

  4. [4]

    Reconstruction and representation of 3d objects with radial basis functions

    Jonathan C Carr, Richard K Beatson, Jon B Cherrie, Tim J Mitchell, W Richard Fright, Bruce C McCallum, and Tim R Evans. Reconstruction and representation of 3d objects with radial basis functions. InProceedings of the 28th annual con- ference on Computer graphics and interactive techniques , pages 67–76, 2001. 2

    work page 2001

  5. [5]

    Efficient geometry-aware 3d generative adversarial networks

    Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 16123–16133, 2022. 2, 3

    work page 2022

  6. [6]

    Chang, Thomas A

    Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu. Shapenet: An information-rich 3d model repository. CoRR, abs/1512.03012, 2015. 5, 6, 7

    arXiv 2015

  7. [7]

    Latent par- tition implicit with surface codes for 3d representation

    Chao Chen, Yu-Shen Liu, and Zhizhong Han. Latent par- tition implicit with surface codes for 3d representation. In European Conference on Computer Vision, pages 322–343. Springer, 2022. 6

    work page 2022

  8. [8]

    Gridpull: To- wards scalability in learning implicit representations from 3d point clouds

    Chao Chen, Yu-Shen Liu, and Zhizhong Han. Gridpull: To- wards scalability in learning implicit representations from 3d point clouds. In Proceedings of the ieee/cvf international conference on computer vision , pages 18322–18334, 2023. 1, 2, 4, 6

    work page 2023

Show all 40 references
  1. [9]

    Energy-based residual latent transport for unsupervised point cloud completion

    Ruikai Cui, Shi Qiu, Saeed Anwar, Jing Zhang, and Nick Barnes. Energy-based residual latent transport for unsupervised point cloud completion. arXiv preprint arXiv:2211.06820, 2022. 1

    2022 arXiv

  2. [10]

    P2c: Self-supervised point cloud completion from single partial clouds

    Ruikai Cui, Shi Qiu, Saeed Anwar, Jiawei Liu, Chaoyue Xing, Jing Zhang, and Nick Barnes. P2c: Self-supervised point cloud completion from single partial clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14351–14360, 2023. 1

    2023

  3. [11]

    Neusdfusion: A spatial-aware generative model for 3d shape completion, reconstruction, and generation

    Ruikai Cui, Weizhe Liu, Weixuan Sun, Senbo Wang, Taizhang Shang, Yang Li, Xibin Song, Han Yan, Zhennan Wu, Shenzhou Chen, et al. Neusdfusion: A spatial-aware generative model for 3d shape completion, reconstruction, and generation. arXiv preprint arXiv:2403.18241, 2024. 3

    2024 arXiv

  4. [12]

    Lam3d: Large image-point-cloud align- ment model for 3d reconstruction from single image

    Ruikai Cui, Xibin Song, Weixuan Sun, Senbo Wang, Weizhe Liu, Shenzhou Chen, Taizhang Shang, Yang Li, Nick Barnes, Hongdong Li, et al. Lam3d: Large image-point-cloud align- ment model for 3d reconstruction from single image. arXiv preprint arXiv:2405.15622, 2024. 2, 3

    2024 arXiv

  5. [13]

    Mitra, and Michael Wimmer

    Philipp Erler, Paul Guerrero, Stefan Ohrhallinger, Niloy J. Mitra, and Michael Wimmer. Points2surf learning implicit surfaces from point clouds. InComputer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part V, pages 108–124. Sprin...

    2020

  6. [14]

    Implicit geometric regularization for learning shapes

    Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099, 2020. 5, 6

    2002 arXiv

  7. [15]

    Consolidation of unorganized point clouds for surface reconstruction

    Hui Huang, Dan Li, Hao Zhang, Uri Ascher, and Daniel Cohen-Or. Consolidation of unorganized point clouds for surface reconstruction. ACM transactions on graphics (TOG), 28(5):1–7, 2009. 2

    2009

  8. [16]

    Poisson surface reconstruction

    Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 2006. 2, 6

    2006

  9. [17]

    Kingma and Jimmy Ba

    Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, 2015. 5

    2015

  10. [18]

    ABC: A big CAD model dataset for geometric deep learning

    Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo. ABC: A big CAD model dataset for geometric deep learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, L...

    2019

  11. [19]

    Octree guided unoriented surface recon- struction

    Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, and Stephen Gould. Octree guided unoriented surface recon- struction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 16717– 16726, 2023. 1

    2023

  12. [20]

    Small steps and level sets: Fitting neural surface models with point guidance

    Chamin Hewa Koneputugodage, Yizhak Ben-Shabat, Dylan Campbell, and Stephen Gould. Small steps and level sets: Fitting neural surface models with point guidance. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21456–21465, 2024. 1, 2, 4

    2024

  13. [21]

    The approximation power of moving least- squares

    David Levin. The approximation power of moving least- squares. Mathematics of computation, 67(224):1517–1531,

  14. [22]

    Implicit filtering for learning neural signed distance functions from 3d point clouds

    Shengtao Li, Ge Gao, Yudong Liu, Ming Gu, and Yu-Shen Liu. Implicit filtering for learning neural signed distance functions from 3d point clouds. In Computer Vision - ECCV 2024 - 18th European Conference, Milan, Italy, September 29-October 4, 2024, Proceedings, Part VI , pages...

    2024

  15. [23]

    Neuralangelo: High-fidelity neural surface reconstruction

    Zhaoshuo Li, Thomas M ¨uller, Alex Evans, Russell H Tay- lor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 8456–8465, 2023. 3

    2023

  16. [24]

    Magic3d: High-resolution 9 text-to-3d content creation

    Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. Magic3d: High-resolution 9 text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognit...

    2023

  17. [25]

    Learning to infer implicit surfaces without 3d supervision

    Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li. Learning to infer implicit surfaces without 3d supervision. Advances in Neural Information Processing Systems , 32,

  18. [26]

    Lorensen and Harvey E

    William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1987, Anaheim, California, USA, July 27-31, 1987 , pages 163–

    1987

  19. [27]

    Neural-pull: Learning signed distance function from point clouds by learning to pull space onto surface

    Baorui Ma, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker. Neural-pull: Learning signed distance function from point clouds by learning to pull space onto surface. In Proceedings of the 38th International Conference on Ma- chine Learning, ICML 2021, 18-24 July 2021, Virtual E...

    2021

  20. [28]

    Surface reconstruction from point clouds by learning predictive context priors

    Baorui Ma, Yu-Shen Liu, Matthias Zwicker, and Zhizhong Han. Surface reconstruction from point clouds by learning predictive context priors. In IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, CVPR 2022, New Or- leans, LA, USA, June 18-24, 2022, pages 6316–6327. IEEE,

    2022

  21. [29]

    Occupancy networks: Learning 3d reconstruction in function space

    Lars Mescheder, Michael Oechsle, Michael Niemeyer, Se- bastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460–4470, 2019. 2, 5

    2019

  22. [30]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM , 65(1):99–106, 2021. 3

    2021

  23. [31]

    Instant neural graphics primitives with a mul- tiresolution hash encoding

    Thomas M ¨uller, Alex Evans, Christoph Schied, and Alexan- der Keller. Instant neural graphics primitives with a mul- tiresolution hash encoding. ACM transactions on graphics (TOG), 41(4):1–15, 2022. 3

    2022

  24. [32]

    Deepsdf: Learning con- tinuous signed distance functions for shape representation

    Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning con- tinuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 165–174, 2019. 2

    2019

  25. [33]

    Shape as points: A dif- ferentiable poisson solver

    Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger. Shape as points: A dif- ferentiable poisson solver. Advances in Neural Information Processing Systems, 34:13032–13044, 2021. 6

    2021

  26. [34]

    Visco grids: Surface reconstruction with viscosity and coarea grids

    Albert Pumarola, Artsiom Sanakoyeu, Lior Yariv, Ali Tha- bet, and Yaron Lipman. Visco grids: Surface reconstruction with viscosity and coarea grids. Advances in Neural Infor- mation Processing Systems, 35:18060–18071, 2022. 6

    2022

  27. [35]

    Pnp-3d: A plug- and-play for 3d point clouds

    Shi Qiu, Saeed Anwar, and Nick Barnes. Pnp-3d: A plug- and-play for 3d point clouds. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1):1312–1319, 2021. 1

    2021

  28. [36]

    Pu-transformer: Point cloud upsampling transformer

    Shi Qiu, Saeed Anwar, and Nick Barnes. Pu-transformer: Point cloud upsampling transformer. In Proceedings of the Asian conference on computer vision , pages 2475–2493,

  29. [37]

    Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Advances in Neural Information Processing Systems 33: An- nual Conference on Neural Information Processin...

    2020

  30. [38]

    Deep geomet- ric prior for surface reconstruction

    Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, and Daniele Panozzo. Deep geomet- ric prior for surface reconstruction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10130–10139, 2019. 6, 7

    2019

  31. [39]

    Blockfusion: Expandable 3d scene gen- eration using latent tri-plane extrapolation

    Zhennan Wu, Yang Li, Han Yan, Taizhang Shang, Weixuan Sun, Senbo Wang, Ruikai Cui, Weizhe Liu, Hiroyuki Sato, Hongdong Li, et al. Blockfusion: Expandable 3d scene gen- eration using latent tri-plane extrapolation. ACM Transac- tions on Graphics (TOG), 43(4):1–17, 2024. 3

    2024

  32. [40]

    Ssp: Semi-signed prioritized neural fitting for surface re- construction from unoriented point clouds

    Runsong Zhu, Di Kang, Ka-Hei Hui, Yue Qian, Shi Qiu, Zhen Dong, Linchao Bao, Pheng-Ann Heng, and Chi-Wing Fu. Ssp: Semi-signed prioritized neural fitting for surface re- construction from unoriented point clouds. InProceedings of the IEEE/CVF Winter Conference on Applications ...

    2024

Pith tools

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