Pith. sign in

REVIEW 4 major objections 4 minor 50 references

Efficient Point Clouds Upsampling via Flow Matching

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read PUFM learns a direct flow from sparse to dense point clouds, reporting state-of-the-art upsampling with only five sampling steps.

desk verdict A real technique-level idea with a strong pre-alignment ablation, but the sampling update contradicts the paper's own Euler equation and must be fixed before the results are credible. read the letter →

arxiv 2501.15286 v1 pith:7F2M32VK submitted 2025-01-25 cs.CV eess.SP

Zhi-Song Liu , Chenhang He , Lei Li This is my paper · ORCID
classification cs.CVeess.SP
keywords pointcloudupsamplingflowmatchingEarthMover'sDistancemidpointinterpolationdiffusionmodelsgenerativemodel3DvisionLiDARclouds
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 proposes PUFM, a flow-matching model for point cloud upsampling that maps a sparse input directly to a high-fidelity dense point cloud instead of first diffusing it into Gaussian noise. Midpoint interpolation densifies the sparse input to match the cardinality of the target, and an Earth Mover's Distance pre-alignment reorders the dense ground truth during training so the learned velocity field follows a stable straight path. On the PUGAN and PU1K benchmarks, the authors report better Chamfer, Hausdorff, and point-to-surface errors than prior diffusion and learning-based upsamplers, and they need only five Euler sampling steps compared with thirty for the diffusion baseline PUDM. The same model transfers to ScanNet RGB-D and KITTI LiDAR inputs, which is the practical payoff: fast, geometry-aware upsampling for real sensors.

What carries the argument

The central object is the learned velocity field $\nu_\theta(x_t,t)$, trained so that integrating it transports the sparse-point distribution to the dense-point distribution. Three pieces make that training tractable: midpoint interpolation (Eq. 5) densifies the sparse cloud to match cardinality, EMD pre-alignment (Eq. 8) permutes the dense ground truth so the residual $x_1-\tilde{x}_0$ is a coherent velocity target, and the straight interpolant $x_t=(1-t)\tilde{x}_0+t x_1$ turns Eq. 6 into a simple regression on the displacement. A cosine schedule over $t$ concentrates training near small $t$, where the motion direction is easiest to pin down.

What would settle it

Compare PUFM's output on the same input processed with and without an EMD re-ordering at inference: if the Chamfer distance changes by more than the training-time margin, the learned flow depends on the training-time matching and will not transfer to arbitrary unaligned patches. A sweep of $\eta$ in Eq. 5 across, say, 0.005 to 0.05 would also show whether the robustness claims hold outside an unspecified noise regime.

Watch

Extended reading notes

Core claim

PUFM's central claim is that the optimal transport between the sparse-point distribution and the dense-point distribution can be learned directly, without a noise-to-data detour. The paper parameterizes a velocity field $\nu_\theta(x_t,t)$ and trains it to reproduce the residual $x_1-\tilde{x}_0$ along the straight interpolant $x_t=(1-t)\tilde{x}_0+t x_1$, where $\tilde{x}_0$ is the midpoint-interpolated sparse cloud. Because point clouds are unordered, the naive residual is ill-defined; the authors fix this with EMD pre-alignment (Eq. 8), which permutes the dense cloud to minimize the bijective assignment cost to the sparse cloud during training, and they show this removes the early-stage collapse seen without it. With the aligned objective, PUFM reports state-of-the-art CD, HD, and P2F on PUGAN and PU1K at 4x and 16x upsampling, and consistent gains over Grad-PU and PUDM across 5x to 32x scales. The efficiency claim is the five-step sampler: a single Euler pass from the sparse prior, rather than a long reverse diffusion chain.

Load-bearing premise

The load-bearing premise is that the velocity field trained on EMD-pre-aligned synthetic pairs transfers to unaligned sparse inputs and to the noisy, nonuniform distributions found in real LiDAR and RGB-D data, even though pre-alignment is applied only during training and the noise level $\eta$ in Eq. 5 is never specified.

Editorial extensions

If this is right

  • If PUFM is correct, diffusion is not needed for point cloud upsampling: direct sparse-to-dense flow matching reaches better CD/HD/P2F with five Euler steps, versus thirty for PUDM.
  • The EMD pre-alignment is not optional: removing it raises PUGAN CD from 1.049 to 2.817 and PU1K CD from 0.545 to 1.188, confirming the matching stabilizes early training.
  • Because quality stays ahead across 5x to 32x factors, the method supports arbitrary upsampling by iterating 4x steps with FPS downsampling.
  • The model's robustness to Gaussian noise at $\eta=0.01$ and $0.02$ and its qualitative results on ScanNet and KITTI imply the learned flow carries over to real RGB-D and LiDAR inputs.

Reading between the lines

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

  • The pre-alignment is a training-only crutch; a direct test would measure how output quality degrades as input patch ordering or noise shifts away from the EMD assignments seen in training.
  • Because the velocity field is defined for all $t\in[0,1]$, the same midpoint-interpolation plus flow-matching recipe could apply to point cloud completion, denoising, or any unordered-set transport problem, not just upsampling.
  • The noise level $\eta$ in Eq. 5 is never fixed in the main text, so the effective source distribution is under-specified; an ablation across $\eta$ values would clarify whether the reported robustness is tied to a particular noise schedule.
Share X Bluesky LinkedIn Reddit HN

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 PUFM, a flow-matching model for point cloud upsampling that maps a sparse point cloud directly to a dense point cloud. The method first densifies the sparse input via midpoint interpolation, pre-aligns the interpolated points to the dense ground truth using Earth Mover's Distance during training, and then learns a conditional velocity field with a flow-matching objective. At inference, the learned ODE is integrated from the interpolated sparse input without alignment. Experiments on PUGAN and PU1K report state-of-the-art Chamfer Distance, Hausdorff Distance, and Point-to-Surface metrics at 4x and 16x upsampling, with only 5 sampling steps, and additional experiments on noisy inputs, arbitrary upsampling factors, ScanNet, and KITTI are presented.

Significance. If the results are reproducible, the paper makes a useful contribution: it demonstrates that a flow-matching formulation with EMD pre-alignment can achieve competitive or better point cloud upsampling quality than diffusion baselines at a fraction of the sampling cost. The ablation 'Ours w/o EMD' (CD 2.817 vs 1.049 on PUGAN) gives clear evidence that the pre-alignment is important for the method's performance, and the extension to real-world RGB-D and LiDAR data is a strength. However, the paper's central reproducibility is undermined by an inconsistency between the stated Euler integrator and the sampling pseudocode, and by unspecified training hyperparameters. The absence of error bars or statistical tests also leaves several reported margins (e.g., PU1K 4x HD: 0.556 vs 0.583) without uncertainty quantification.

major comments (4)
  1. [Section 3.2, Eq. (4) vs Algorithm 2] The sampling update in Algorithm 2, x_{t+1} = (1 - δ_t) x_t + δ_t ν_θ(x_t, t), is not the Euler step defined in Eq. (4), x_{t+1} = x_t + (δ_{t+1} - δ_t) ν_θ(x_t, t). These are two different discrete processes: the former includes an extra -δ_t x_t term and is not an Euler integration of the learned ODE. The manuscript does not state which update was used to produce Tables 1-4 and the 5-step runtime in Table 3. If Algorithm 2 was used, the reported results are not grounded in the flow-matching objective of Eq. (6); if Eq. (4) was used, the pseudocode is wrong and the method is underspecified without code. This is a correctness-level gap that must be resolved before the central claims can be accepted.
  2. [Section 3.2, Eq. (8) and Algorithm 2] The paper trains the velocity field on EMD-pre-aligned pairs (Eq. 8, Algorithm 1) but at inference applies no alignment (Algorithm 2, Section 3.2). This is a distribution shift: the network must generalize from aligned training pairs to unaligned, possibly noisy or nonuniform sparse inputs. The manuscript does not analyze or quantify this shift, and the toy example in Figure 3 does not address it. Since the central claim is that PUFM 'directly learns the optimal transport between sparse and dense point cloud distributions,' the authors should provide evidence—for example, by comparing aligned vs unaligned inputs at inference, or by studying how the learned trajectories degrade with increasing misalignment—that the trained model transfers to raw unaligned inputs.
  3. [Section 4.5, Table 4] In Table 4, the P2F values at noise level η=0.02 (e.g., PUDM 1.150, Ours 1.055) are about five times smaller than at η=0.01 (e.g., PUDM 6.025, Ours 5.887), while CD and HD degrade as expected with more noise. This internal inconsistency is not explained and undermines the robustness claim. The authors should either correct the metric values, explain why P2F behaves this way, or remove the affected rows.
  4. [Section 3.2, Eq. (5)] The midpoint interpolation in Eq. (5) depends on two hyperparameters: the repeat factor γ and the noise level η. Neither is specified in the main text, and the supplementary is referenced only for the model architecture and sample schemes, not for these values. Without these values, the training-time source distribution is not fully specified, and the robustness experiments in Section 4.5 appear to use different η values than the main training setup. Please report the default η and γ used for training and testing.
minor comments (4)
  1. [Section 4.2, Table 1] The paper does not report error bars, standard deviations, or significance tests. Several margins in Table 1 are small (e.g., PU1K 4x HD: Ours 0.556 vs Grad-PU 0.583), so it would strengthen the comparison to include variance over multiple runs or seeds.
  2. [Section 2 (References)] The reference style uses 'and et al.' for many entries, which makes it hard to identify authors and papers. Please use a standard bibliography format with full author lists or at least first-author names.
  3. [Section 4.1, Dataset description] The paper states 'we use Poisson disk sampling to generate uniform patches as ground truth' but does not specify the patch size or the exact protocol for extracting sparse inputs beyond randomly sampling 256 points. Providing the patch radius and sampling parameters would improve reproducibility.
  4. [Section 3.2, Eq. (6)] Equation (6) writes L(θ) = min_θ E ||...||, but the min is part of the optimization problem, not the loss definition. The equation should be L(θ) = E ||...||, or the min should be placed outside the expectation in the training objective.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the flow-matching derivation is self-contained and the benchmark metrics are external to the training objective.

full rationale

No circular step is present. The method is a standard conditional flow-matching formulation: Eq. (6) regresses the network on the explicit straight-path velocity x1 - x~0 defined in Eq. (7), and the EMD pre-alignment of Eq. (8) only chooses a training-time permutation of the dense ground truth; it is not applied at inference. The reported Chamfer Distance, Hausdorff Distance, and Point-to-Surface metrics are external geometric losses computed on held-out test clouds, so the state-of-the-art claims are not algebraic consequences of the training loss. No fitted parameter is renamed as a prediction, and no load-bearing conclusion depends on a self-citation; the only self-citation (Liu et al., 2023, PUBP) appears in related work and baseline comparisons. The discrepancy between the Euler update in Eq. (4) and the update in Algorithm 2 is an internal consistency and reproducibility concern, not a circularity, because neither update defines the evaluation metric in terms of the training targets.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method adds no new physical entities. Its central dependencies are the assumption that sparse and dense clouds share one underlying geometry, the validity of midpoint interpolation as a source densifier, the usefulness of EMD alignment, and the network's ability to generalize from aligned training pairs to unaligned test inputs. Several hyperparameters (noise scale eta, repeat factor gamma, time schedule, number of sampling steps) are chosen rather than fitted to the test data; still, they are not reported with full settings, which matters for reproduction.

free parameters (4)
  • Input noise scale eta = Not reported in main text; robustness tests use eta=0.01 and 0.02
    Added in Eq. 5 to the midpoint-interpolated source. The training value is never specified, and output quality depends on this choice.
  • Repeat factor gamma = 4 for 4x upsampling
    Determines how many points result from midpoint interpolation. Chosen by construction to match target cardinality rather than fitted to data.
  • Time schedule t = 1 - cos(s pi/2) = s ~ U[0,1]
    Chosen to emphasize early timesteps and stated as important for performance. The comparison of different schedules is deferred to the missing supplementary.
  • Number of sampling steps = 5
    Reported as the optimal number for PUFM in Table 3 and used for runtime comparison. The central efficiency claim depends on this choice.
assumptions (5)
  • domain assumption Sparse point clouds are partial views of dense point clouds sharing the same underlying 3D structure
    Stated at the start of Sec. 3.2 and used to justify mapping X_sparse to X_dense rather than starting from Gaussian noise. Violated if input is heavily occluded or captured by a different sensor.
  • domain assumption Midpoint interpolation plus Gaussian noise produces a valid densified source distribution
    Eq. 5 defines the source used in training. No evidence is given that repeated and farthest-point-sampled points plus noise represent the true conditional distribution of dense points given sparse points, and the noise scale is unspecified.
  • standard math EMD optimization gives a coherent bijective pairing between source and target clouds
    Eq. 8 relies on an Earth Mover's Distance solver to produce the optimal assignment. This is a standard algorithmic assumption, though the solver details are omitted.
  • domain assumption The PointNet++ based network can express the flow velocity field
    The architecture is borrowed from prior work and is not analyzed. Its capacity to represent the required velocity field is assumed without proof.
  • ad hoc to paper Flow learned on pre-aligned pairs transfers to raw unaligned inputs at inference
    Pre-alignment is train-only, and the paper provides no analysis of the resulting train/inference distribution shift, which is central to the method's validity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Efficient Point Clouds Upsampling via Flow Matching." pith.science (2026). https://pith.science/paper/7F2M32VK

@misc{pith2026250115286,
  author       = {Pith},
  title        = {Pith review of: Efficient Point Clouds Upsampling via Flow Matching},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7F2M32VK}},
  note         = {Machine review of arXiv:2501.15286}
}
read the original abstract

Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds, overlooking the geometric information inherent in sparse point clouds. To address these inefficiencies, we propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method first employs midpoint interpolation to sparse point clouds, resolving the density mismatch between sparse and dense point clouds. Since point clouds are unordered representations, we introduce a pre-alignment method based on Earth Mover's Distance (EMD) optimization to ensure coherent interpolation between sparse and dense point clouds, which enables a more stable learning path in flow matching. Experiments on synthetic datasets demonstrate that our method delivers superior upsampling quality but with fewer sampling steps. Further experiments on ScanNet and KITTI also show that our approach generalizes well on RGB-D point clouds and LiDAR point clouds, making it more practical for real-world applications.

Figures

Figures reproduced from arXiv: 2501.15286 by the authors.

Figure 1
Figure 1. Convergence comparison among different distribution mapping paths. The diffusion model PUDM demonstrates slow convergence since it starts from noise distribution. Our proposed PUFM learns flow matching from sparse to dense point clouds. Pre￾alignment is applied to minimize the learning ambiguity at the early stage, resulting in a more efficient upsampling process. often rely on computationally expensive optimization… view at source ↗
Figure 2
Figure 2. Illustration of PUFM. It processes point clouds as patches (yellow circles), and learns Dθ to map the distribution from sparse to dense patches. It contains forward alignment and interpolation (a) and backward restoration and sampling (b). In (a), the model first pre-aligns the sparse (initially upsampled by midpoint interpolation) and dense point clouds and then randomly picks “noisy” points as xαi with the time st… view at source ↗
Figure 3
Figure 3. A toy example on flow matching for point cloud transformation. Without pre-alignment, the model converges slower as it first transforms the source point clouds to a set of dispersed clusters and then gradually matches to the target point clouds. In contrast, the pre-alignment exhibits more efficient and consistent transformation. densified version x˜0 = mid(x0, η) can be defined as: x˜0 = 1 2 · [Rγ(x0) + FPS(x0, γ)]… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Visual comparison of different methods on 4× upsampling. We zoom in on the red box regions to highlight the point cloud upsampling differences. We also show the mesh reconstruction, and ours produces evenly distributed point clouds and smooth meshes. 5 7 8 12 20 2…
Figure 6
Figure 6. Figure 6: Visulization of noisy point cloud upsampling. We cal￾culate the P2F distance as the color feature to the point cloud for visualization. Ours are visually better than baselines, without show￾ing holes in (a) and noisy outliers in (b). and InDI [Delbracio and Milanfar, 2…
Figure 7
Figure 7. Figure 7: Sample visualization at different time steps. We com￾pare with DDPM, PUDM, and ours at different sampling steps. Ours quickly learns the optimal upsampling point clouds, while others take longer sampling steps (> 10). Dataset PUGAN PU1K Running time (s) CD HD P2F CD HD…
Figure 8
Figure 8. Figure 8: Visulization of real point cloud upsampling. We use two examples from ScanNet and KITTI to apply 4× upsampling. Compared to PUDM, ours does not show inconsistent patterns on the bicyclist or noisy point distribution in the living room. 5 Conclusion In this paper, we in…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

50 extracted references · 47 canonical work pages

  1. [1]

    Bernardini and et al

    [Bernardini and et al., 1999] F. Bernardini and et al. The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Vis. Comput. Graph., 5(4):349–359,

    work page 1999

  2. [6]

    Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner

    [Dai et al., 2017] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE,

    work page 2017

  3. [7]

    Inversion by direct iteration: An alternative to denoising diffusion for image restoration

    [Delbracio and Milanfar, 2023] Mauricio Delbracio and Peyman Milanfar. Inversion by direct iteration: An alternative to denoising diffusion for image restoration

    work page 2023

  4. [8]

    Neural points: Point cloud representation with neural fields for arbitrary upsampling

    [Feng and et al., 2022] Wanquan Feng and et al. Neural points: Point cloud representation with neural fields for arbitrary upsampling. In IEEE Conf. Comput. Vis. Pattern Recog.,

    work page 2022

  5. [12]

    Grad-pu: Arbitrary- scale point cloud upsampling via gradient descent with learned distance functions

    [He and et al., 2023] Yun He and et al. Grad-pu: Arbitrary- scale point cloud upsampling via gradient descent with learned distance functions. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5354–5363,

    work page 2023

  6. [13]

    Denoising diffu- sion probabilistic models

    [Ho and et al., 2020] Jonathan Ho and et al. Denoising diffu- sion probabilistic models. In Adv. Neural Inform. Process. Syst.,

    work page 2020

  7. [14]

    Surface reconstruction from unorganized points

    [Hoppe and et al., 1992] Hugues Hoppe and et al. Surface reconstruction from unorganized points. InProceed. Com- puter Graphics and Interactive Techniques , page 71–78,

    work page 1992

  8. [16]

    Tp- node: Topology-aware progressive noising and denoising of point clouds towards upsampling

    [Kumbar and et al., 2023] Akash Kumbar and et al. Tp- node: Topology-aware progressive noising and denoising of point clouds towards upsampling. In Int. Conf. Comput. Vis., pages 2264–2274,

    work page 2023

Show all 50 references
  1. [17]

    and et al., 2007] Yaron L

    [L. and et al., 2007] Yaron L. and et al. Parameterization- free projection for geometry reconstruction. In ACM SIG- GRAPH, page 22–es, New York, NY , USA,

    2007

  2. [20]

    [Li et al., 2019] R. Li, X. Li, C. Fu, D. Cohen-Or, and P. Heng. Pu-gan: A point cloud upsampling adversarial network. Int. Conf. Comput. Vis., pages 7202–7211, nov

    2019

  3. [22]

    Flow matching for generative modeling

    [Lipman and et al., 2023] Yaron Lipman and et al. Flow matching for generative modeling. In The Eleventh Inter- national Conference on Learning Representations,

    2023

  4. [23]

    Flow match- ing for generative modeling

    [Lipman et al., 2023] Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow match- ing for generative modeling. In Int. Conf. Learn. Repre- sent.,

    2023

  5. [24]

    Arbitrary point cloud upsampling via dual back-projection network

    [Liu and et al., 2023] Zhi-Song Liu and et al. Arbitrary point cloud upsampling via dual back-projection network. In IEEE Int. Conf. Image Process., pages 1470–1474,

    2023

  6. [25]

    Low rank matrix approximation for 3d geometry filtering

    [Lu and et al., 2022] Xuequan Lu and et al. Low rank matrix approximation for 3d geometry filtering. IEEE Trans. Vis. Comput. Graph., 28(4):1835–1847,

    2022

  7. [26]

    Diffusion probabilistic models for 3d point cloud generation

    [Luo and Hu, 2021] Shitong Luo and Wei Hu. Diffusion probabilistic models for 3d point cloud generation. In IEEE Conf. Comput. Vis. Pattern Recog., June

    2021

  8. [27]

    Rethinking network design and local geometry in point cloud: A simple resid- ual mlp framework

    [Ma and et al., 2022] Xu Ma and et al. Rethinking network design and local geometry in point cloud: A simple resid- ual mlp framework. Int. Conf. Learn. Represent.,

    2022

  9. [28]

    Pu-flow: A point cloud upsampling network with normalizing flows

    [Mao et al., 2023] Aihua Mao, Zihui Du, Junhui Hou, Yaqi Duan, Yong-Jin Liu, and Ying He. Pu-flow: A point cloud upsampling network with normalizing flows. IEEE Trans. Vis. Comput. Graph., 29(12):4964–4977,

    2023

  10. [29]

    Self- sampling for neural point cloud consolidation

    [Metzer and et al., 2021] Gal Metzer and et al. Self- sampling for neural point cloud consolidation. 40(5),

    2021

  11. [30]

    Fast point transformer

    [Park and et al., 2022] Chunghyun Park and et al. Fast point transformer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16949–16958, June

    2022

  12. [31]

    Qi and et al

    [Qi and et al., 2017] Charles R. Qi and et al. Pointnet++: deep hierarchical feature learning on point sets in a met- ric space. In Adv. Neural Inform. Process. Syst., NIPS’17, page 5105–5114,

    2017

  13. [32]

    Pugeo-net: A geometry-centric network for 3d point cloud upsampling

    [Qian and et al., 2020] Yue Qian and et al. Pugeo-net: A geometry-centric network for 3d point cloud upsampling. arXiv, abs/2002.10277,

    2020 arXiv

  14. [33]

    Pu-gcn: Point cloud upsampling using graph convolutional net- works

    [Qian and et al., 2021] Guocheng Qian and et al. Pu-gcn: Point cloud upsampling using graph convolutional net- works. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11683–11692, June

    2021

  15. [34]

    Pointnext: revisiting pointnet++ with improved training and scaling strategies

    [Qian and et al., 2024] Guocheng Qian and et al. Pointnext: revisiting pointnet++ with improved training and scaling strategies. In Adv. Neural Inform. Process. Syst. , NIPS ’22,

    2024

  16. [35]

    A conditional de- noising diffusion probabilistic model for point cloud up- sampling

    [Qu and et al., 2024] Wentao Qu and et al. A conditional de- noising diffusion probabilistic model for point cloud up- sampling. In IEEE Conf. Comput. Vis. Pattern Recog. , pages 20786–20795,

    2024

  17. [36]

    Repkpu: Point cloud upsampling with kernel point representation and de- formation

    [Rong and et al., 2024] Yi Rong and et al. Repkpu: Point cloud upsampling with kernel point representation and de- formation. In IEEE Conf. Comput. Vis. Pattern Recog. , pages 21050–21060,

    2024

  18. [37]

    Singh and et al

    [Singh and et al., 2007] G. Singh and et al. Guest editors’ in- troduction: Special section on acm vrst

    2007

  19. [38]

    Kp- conv: Flexible and deformable convolution for point clouds

    [Thomas and et al., 2019] Hugues Thomas and et al. Kp- conv: Flexible and deformable convolution for point clouds. Int. Conf. Comput. Vis.,

    2019

  20. [39]

    Atten- tion is all you need

    [Vaswani and et al., 2017] Ashish Vaswani and et al. Atten- tion is all you need. In Adv. Neural Inform. Process. Syst., volume 30, pages 5998–6008,

    2017

  21. [40]

    P2p-bridge: Diffusion bridges for 3d point cloud denoising

    [V ogel and et al., 2024] Mathias V ogel and et al. P2p-bridge: Diffusion bridges for 3d point cloud denoising. In Eur. Conf. Comput. Vis.,

    2024

  22. [41]

    Dynamic graph cnn for learning on point clouds

    [Wang and et al., 2019] Yue Wang and et al. Dynamic graph cnn for learning on point clouds. ACM Trans. Graph. , 38(5), oct

    2019

  23. [42]

    Self- supervised arbitrary-scale point clouds upsampling via im- plicit neural representation

    [Wenbo and et al., 2022] Zhao Wenbo and et al. Self- supervised arbitrary-scale point clouds upsampling via im- plicit neural representation. In IEEE Conf. Comput. Vis. Pattern Recog.,

    2022

  24. [43]

    Point trans- former v2: Grouped vector attention and partition-based pooling

    [Wu and et al., 2022] Xiaoyang Wu and et al. Point trans- former v2: Grouped vector attention and partition-based pooling. In Adv. Neural Inform. Process. Syst.,

    2022

  25. [44]

    Point transformer v2: Grouped vector attention and partition-based pooling

    [Wu et al., 2022] Xiaoyang Wu, Yixing Lao, Li Jiang, Xi- hui Liu, and Hengshuang Zhao. Point transformer v2: Grouped vector attention and partition-based pooling. In Adv. Neural Inform. Process. Syst.,

    2022

  26. [45]

    Visual point cloud forecasting enables scalable autonomous driving

    [Yang and et al., 2024] Zetong Yang and et al. Visual point cloud forecasting enables scalable autonomous driving. In IEEE Conf. Comput. Vis. Pattern Recog.,

    2024

  27. [46]

    Yifan and et al

    [Yifan and et al., 2019] W. Yifan and et al. Patch-based pro- gressive 3d point set upsampling.IEEE Conf. Comput. Vis. Pattern Recog., pages 5951–5960, jun

    2019

  28. [47]

    Yu and et al

    [Yu and et al., 2018] L. Yu and et al. Pu-net: Point cloud upsampling network. IEEE Conf. Comput. Vis. Pattern Recog., pages 2790–2799, jun

    2018

  29. [49]

    Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features

    [Zhang and et al., 2019] Kuangen Zhang and et al. Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features. ArXiv, abs/1904.10014,

    2019 arXiv

  30. [50]

    Point transformer

    [Zhao et al., 2021] Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip HS Torr, and Vladlen Koltun. Point transformer. In Int. Conf. Comput. Vis., pages 16259–16268, 2021

    2021

  31. [1992]

    3d gaus- sian splatting for real-time radiance field rendering

    [Kerbl and et al., 2023] Bernhard Kerbl and et al. 3d gaus- sian splatting for real-time radiance field rendering. ACM Trans. Graph., 42(4), July

    2023

  32. [1999]

    Charles, H

    [Charles et al., 2017] R. Charles, H. Su, M. Kaichun, and L. J. Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In IEEE Conf. Com- put. Vis. Pattern Recog., pages 77–85, Los Alamitos, CA, USA, jul

    2017

  33. [2007]

    Spot- compose: A framework for open-vocabulary object re- trieval and drawer manipulation in point clouds

    [Lemke and et al., 2024] Oliver Lemke and et al. Spot- compose: A framework for open-vocabulary object re- trieval and drawer manipulation in point clouds. In Int. Conf. on Robotics and Automation,

    2024

  34. [2013]

    [Guo, 2021] et al. Guo, MH. Pct: Point cloud transformer. Comp. Visual Media, (7):187–199,

    2021

  35. [2017]

    Pointmixup: Augmentation for point clouds

    [Chen and et al., 2020] Yunlu Chen and et al. Pointmixup: Augmentation for point clouds. InEur. Conf. Comput. Vis.,

    2020

  36. [2018]

    Pointr: Diverse point cloud completion with geometry-aware transform- ers

    [Yu and et al., 2021] Xumin Yu and et al. Pointr: Diverse point cloud completion with geometry-aware transform- ers. In Int. Conf. Comput. Vis.,

    2021

  37. [2019]

    Joint point cloud upsampling and cleaning with octree- based cnns

    [Li et al., 2024] Jihe Li, Bo Pang, and Peng-Shuai Wang. Joint point cloud upsampling and cleaning with octree- based cnns. arXiv preprint arXiv:2410.17001,

    2024 arXiv

  38. [2020]

    Deep point set resampling via gradient fields

    [Chen and et al., 2023] Haolan Chen and et al. Deep point set resampling via gradient fields. IEEE Trans. Pattern Anal. Mach. Intell., 45(3):2913–2930,

    2023

  39. [2021]

    Point2mesh: A self-prior for deformable meshes

    [Hanocka and et al., 2020] Rana Hanocka and et al. Point2mesh: A self-prior for deformable meshes. ACM Trans. Graph., 39(4),

    2020

  40. [2022]

    Vision meets robotics: The kitti dataset

    [Geiger and et al., 2013] Andreas Geiger and et al. Vision meets robotics: The kitti dataset. International Journal of Robotics Research (IJRR),

    2013

  41. [2023]

    Pointcept: A codebase for point cloud perception research

    [Contributors, 2023] Pointcept Contributors. Pointcept: A codebase for point cloud perception research. https:// github.com/Pointcept/Pointcept,

    2023

  42. [2024]

    Point cloud upsam- pling via disentangled refinement

    [Li and et al., 2021] Ruihui Li and et al. Point cloud upsam- pling via disentangled refinement. In IEEE Conf. Comput. Vis. Pattern Recog.,

    2021

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.