Pith. sign in

REVIEW 1 major objections 1 minor 41 references

Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection

T0 review · 1 major / 1 minor · reviewed 2026-06-25 · grok-4.3

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

Pith's one-line read Point cloud diffusion with multi-modal instance conditioning generates weak defects and joint reconstruction detects them without background distortion.

desk verdict PCDiff applies diffusion with multi-modal instance conditioning and joint reconstruction to target weak 3D defects and background bias, but the abstract gives no numbers or details to check the outperformance claims. read the letter →

arxiv 2606.25740 v1 pith:LWC5X7FG submitted 2026-06-24 cs.CV cs.AI

Linchun Wu , Qin Zou , Jiwen Lu , Qingquan Li This is my paper · ORCID
classification cs.CVcs.AI
keywords pointcloud3Danomalydetectiondiffusionmodelreconstructionindustrialinspectionmulti-modalconditioningweakdefect
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

The paper presents PCDiff to solve two problems in 3D point cloud anomaly detection for manufacturing. Small defects like scratches produce deviations as tiny as 0.001 after normalization, making them hard to reconstruct, while background areas often shift during reconstruction and create false alarms. PCDiff adds instance-level multi-modal attention to a diffusion model, feeding it texture gradients, image patches, text, and masks so it can create realistic versions of those weak defects. A joint local-global reconstruction step then restores the defects locally while keeping overall geometry consistent globally. Experiments show this combination raises both generation quality and final detection accuracy over prior methods.

What carries the argument

Instance-level multi-modal attention inside the diffusion model for anomaly generation, paired with a joint local-global reconstruction algorithm that separates defect restoration from background preservation.

What would settle it

Apply PCDiff to a set of point clouds containing documented weak defects of normalized deviation around 0.001 and check whether detection recall stays flat or background false positives rise compared with non-diffusion baselines.

Watch

Extended reading notes

Core claim

PCDiff is a point cloud diffusion framework for instance-level 3D anomaly generation and detection. In generation, instance-level multi-modal attention conditions the diffusion process on texture gradient, image patch, text, and mask to produce high-quality weak-defective anomalies. In detection, a joint local-global reconstruction algorithm ensures local anomaly restoration and global geometric consistency, preserving background normal structure while restoring foreground defects.

Load-bearing premise

Conditioning the diffusion process with texture, image, text, and mask data produces accurate weak defects, and the joint reconstruction can restore those defects without shifting the surrounding normal geometry.

Editorial extensions

If this is right

  • Weak-defective anomalies become easier to generate at high fidelity because the multi-modal conditions supply the missing local detail.
  • Background positional bias drops because the reconstruction enforces global geometric consistency alongside local fixes.
  • Anomaly detection accuracy rises substantially on instance-level 3D data once both generation and reconstruction improve.
  • The same pipeline applies directly to other industrial point-cloud inspection tasks that need to flag small surface flaws.

Reading between the lines

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

  • The conditioning strategy might transfer to other 3D modalities such as meshes if equivalent multi-modal signals are available.
  • Detection speed could become real-time for factory lines once the diffusion sampling is distilled or accelerated.
  • Similar diffusion-plus-reconstruction patterns could address weak-signal problems in related tasks like 3D object completion or surface repair.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

1 major / 1 minor

Summary. The paper introduces PCDiff, a point cloud diffusion framework for instance-level 3D anomaly generation and detection. It tackles two challenges in reconstruction-based methods: difficulty in reconstructing weak defective regions (e.g., scratches with deviations as small as 10^{-3}) and positional bias in background regions leading to false positives. The generation phase uses instance-level multi-modal attention conditioned on texture gradient, image patch, text, and mask to generate weak-defective anomalies. The detection phase employs a joint local-global reconstruction algorithm to restore local anomalies while maintaining global geometric consistency. The paper claims that extensive experiments show PCDiff significantly outperforms state-of-the-art methods in 3D anomaly generation fidelity, reconstruction quality, and anomaly detection accuracy.

Significance. If the results hold, this work could have significant impact on high-precision industrial manufacturing by improving the detection of subtle 3D anomalies and reducing false positives, addressing key limitations in existing reconstruction-based approaches.

major comments (1)
  1. [Abstract] Abstract: The central claim of substantial improvements in anomaly detection accuracy relies on experimental outcomes that are not detailed in the provided abstract, including specific metrics, datasets, baselines, and statistical significance, making it difficult to assess the validity of the outperformance.
minor comments (1)
  1. [Abstract] Abstract: The phrasing 'anomalies are conditioned with texture gradient...' could be clarified to 'conditioned on' for precision.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review and the opportunity to clarify our work. We address the single major comment below regarding the abstract.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of substantial improvements in anomaly detection accuracy relies on experimental outcomes that are not detailed in the provided abstract, including specific metrics, datasets, baselines, and statistical significance, making it difficult to assess the validity of the outperformance.

    Authors: We agree that the abstract, as a concise summary, does not include specific quantitative metrics, dataset names, baseline comparisons, or statistical details. These are fully reported in the Experiments section (including tables with mIoU, CD, and detection AUC on MVTec3D-AD and other benchmarks, with comparisons to methods such as PatchCore, BTF, and others, plus significance testing). To address the concern, we will revise the abstract to incorporate key results (e.g., detection accuracy gains and generation fidelity metrics) while respecting length constraints. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The supplied abstract (and referenced full text placeholder) presents PCDiff as a diffusion framework incorporating instance-level multi-modal attention for generation and a joint local-global reconstruction algorithm for detection. No equations, parameter-fitting steps, self-citations, uniqueness theorems, or ansatzes are exhibited that would reduce any claimed prediction or result to its own inputs by construction. Central claims of outperformance rest on experimental outcomes external to any derivation chain, rendering the work self-contained with no load-bearing circular steps.

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

Abstract supplies no explicit free parameters, axioms, or invented entities; all technical details are absent.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection." pith.science (2026). https://pith.science/paper/LWC5X7FG

@misc{pith2026260625740,
  author       = {Pith},
  title        = {Pith review of: Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LWC5X7FG}},
  note         = {Machine review of arXiv:2606.25740}
}
abstract

3D anomaly detection in point clouds is critical for high-precision industrial manufacturing. Reconstruction-based methods have laid a strong foundation by detecting 3D anomalies through comparisons between defective inputs and their reconstructed normal counterparts. However, existing methods still suffer from two challenges: 1) the foreground weak defective regions such as scratches are hard to reconstruct and detect, where the anomaly deviations in normalized point clouds can be as small as $10^{-3}$; 2) the background non-defective regions are prone to get positional bias in reconstruction, which leads to false positives. To address these challenges, we propose \textbf{PCDiff}, a point cloud diffusion framework for instance-level 3D anomaly generation and detection. In the generation phase, an instance-level multi-modal attention is embedded into the generation framework, where anomalies are conditioned with texture gradient, image patch, text and mask. The instance-level condition enables the high-quality generation of weak-defective anomalies. In the detection phase, a joint local-global reconstruction algorithm is introduced to ensure local anomaly restoration and global geometric consistency, which preserves background normal structure while restoring the foreground defect. Extensive experiments demonstrate that the proposed PCDiff significantly outperforms state-of-the-art methods in both 3D anomaly generation fidelity and reconstruction quality, leading to substantial improvements in anomaly detection accuracy.

Figures

Figures reproduced from arXiv: 2606.25740 by the authors.

Figure 1
Figure 1. Two different pipelines for 3D anomaly detection. (a) The conventional [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Comparison of reconstruction results obtained by R3D-AD and [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. An overview of the proposed PCDiff. The left part illustrates texture augmentation process. The input datasets mesh [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: The local-global anomaly detection framework. The local path reconstructs the test point cloud into a mesh, renders multi-view images, and estimates [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: 3D anomaly generation results obtained by the proposed PCDiff. Top: textured 3D anomaly samples. Bottom: the generated multi-view images. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Visual comparison of detection results obtained by different methods on Anomaly-ShapeNet and Real3D-AD datasets. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 7
Figure 7. Figure 7: The visualization of reconstruction and detection results on [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 6
Figure 6. Figure 6: As shown, PatchCore tends to highlight anomaly [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 8
Figure 8. Figure 8: The visualization of failure mesh reconstruction and 2D detection [PITH_FULL_IMAGE:figures/full_fig_p009_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

41 extracted references · 5 canonical work pages

  1. [1]

    Mgld-tlnet: Multigeometric and long-distance represen- tation network for transmission line inspection,

    R. Du, H. Zhang, K. Zhang, B. Biekezati, H. Zhong, J. Yi, J. Mao, and Y . Wang, “Mgld-tlnet: Multigeometric and long-distance represen- tation network for transmission line inspection,”IEEE Transactions on Cybernetics, pp. 1–14, 2026

    2026

  2. [2]

    Real3d-ad: A dataset of point cloud anomaly detection,

    J. Liu, G. Xie, R. Chen, X. Li, J. Wang, Y . Liu, C. Wang, and F. Zheng, “Real3d-ad: A dataset of point cloud anomaly detection,”Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  3. [3]

    Industrial foundation model,

    L. Ren, H. Wang, J. Dong, Z. Jia, S. Li, Y . Wang, Y . Laili, D. Huang, L. Zhang, and B. Li, “Industrial foundation model,”IEEE Transactions on Cybernetics, vol. 55, no. 5, pp. 2286–2301, 2025

    2025

  4. [4]

    Cloud-based li-ion battery anomaly detection, localization and classification,

    A. Tang, Z. Wu, Y . Xu, K. Liu, and Q. Yu, “Cloud-based li-ion battery anomaly detection, localization and classification,”IEEE Transactions on Industrial Informatics, 2024

    2024

  5. [5]

    Anomaly detection and fault classification of printed circuit boards based on multimodal features of the infrared thermal imaging,

    Z. Wang, H. Yuan, J. Lv, C. Liu, H. Xu, and J. Li, “Anomaly detection and fault classification of printed circuit boards based on multimodal features of the infrared thermal imaging,”IEEE Transactions on Instru- mentation and Measurement, vol. 73, pp. 1–13, 2024

    2024

  6. [6]

    Vtfusion: A vision–text multimodal fusion network for few-shot anomaly detection,

    Y . Jiang, Y . Cao, Y . Cheng, Y . Zhang, and W. Shen, “Vtfusion: A vision–text multimodal fusion network for few-shot anomaly detection,” IEEE Transactions on Cybernetics, pp. 1–10, 2026

    2026

  7. [7]

    Im-iad: Industrial image anomaly detection benchmark in manufacturing,

    G. Xie, J. Wang, J. Liu, J. Lyu, Y . Liu, C. Wang, F. Zheng, and Y . Jin, “Im-iad: Industrial image anomaly detection benchmark in manufacturing,”IEEE Transactions on Cybernetics, vol. 54, no. 5, pp. 2720–2733, 2024

    2024

  8. [8]

    Look inside for more: Internal spatial modality perception for 3d anomaly detection,

    H. Liang, G. Xie, C. Hou, B. Wang, C. Gao, and J. Wang, “Look inside for more: Internal spatial modality perception for 3d anomaly detection,” inProceedings of the AAAI Conference on Artificial Intelligence, vol. 39, no. 5, 2025, pp. 5146–5154

    2025

Show all 41 references
  1. [9]

    Quality control in extrusion-based additive manufacturing: A review of machine learning approaches,

    A. G. Pereira, G. F. Barbosa, M. G. Filho, S. B. Shiki, and A. L. d. Silva, “Quality control in extrusion-based additive manufacturing: A review of machine learning approaches,”IEEE Transactions on Cybernetics, vol. 55, no. 6, pp. 2522–2534, 2025

    2025

  2. [10]

    Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network,

    W. Li, X. Xu, Y . Gu, B. Zheng, S. Gao, and Y . Wu, “Towards scalable 3d anomaly detection and localization: A benchmark via 3d anomaly synthesis and a self-supervised learning network,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 22 207–22 216

    2024

  3. [11]

    Asymmetric student-teacher networks for industrial anomaly detection,

    M. Rudolph, T. Wehrbein, B. Rosenhahn, and B. Wandt, “Asymmetric student-teacher networks for industrial anomaly detection,” inIEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 2592– 2602

    2023

  4. [12]

    R3d-ad: Reconstruction via diffusion for 3d anomaly detection,

    Z. Zhou, L. Wang, N. Fang, Z. Wang, L. Qiu, and S. Zhang, “R3d-ad: Reconstruction via diffusion for 3d anomaly detection,” inEuropean conference on computer vision. Springer, 2024, pp. 91–107

    2024

  5. [13]

    Examining the source of defects from a mechanical perspective for 3d anomaly detection,

    H. Liang, A. Wang, J. Zhou, X. Jin, C. Gao, and J. Wang, “Examining the source of defects from a mechanical perspective for 3d anomaly detection,”arXiv preprint arXiv:2505.05901, 2025

    2025

  6. [14]

    Dual-interrelated diffusion model for few-shot anomaly image generation,

    Y . Jin, J. Peng, Q. He, T. Hu, J. Wu, H. Chen, H. Wang, W. Zhu, M. Chi, J. Liuet al., “Dual-interrelated diffusion model for few-shot anomaly image generation,” inProceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 30 420–30 429

    2025

  7. [15]

    Po3ad: Predicting point offsets toward better 3d point cloud anomaly detection,

    J. Ye, W. Zhao, X. Yang, G. Cheng, and K. Huang, “Po3ad: Predicting point offsets toward better 3d point cloud anomaly detection,” inPro- ceedings of the Computer Vision and Pattern Recognition Conference, 2025, pp. 1353–1362

    2025

  8. [16]

    Scalable 3d captioning with pretrained models,

    T. Luo, C. Rockwell, H. Lee, and J. Johnson, “Scalable 3d captioning with pretrained models,”Advances in Neural Information Processing Systems, vol. 36, 2024

    2024

  9. [17]

    Recurrent diffusion for 3d point cloud generation from a single image,

    Y . Zhou, D. Ye, H. Zhang, X. Xu, H. Sun, Y . Xu, X. Liu, and Y . Zhou, “Recurrent diffusion for 3d point cloud generation from a single image,” IEEE Transactions on Image Processing, vol. 34, pp. 1753–1765, 2025

    2025

  10. [18]

    Wssic-net: Weakly-supervised semantic instance completion of 3d point cloud scenes,

    Z. Fu, Y . Guo, M. Chen, Q. Hu, H. Laga, F. Boussaid, and M. Ben- namoun, “Wssic-net: Weakly-supervised semantic instance completion of 3d point cloud scenes,”IEEE Transactions on Image Processing, vol. 34, pp. 2008–2019, 2025

    2008

  11. [19]

    Hifi3d: Improving text-to-3d with high-fidelity multi-view diffusion,

    R. Liu, Y . Chen, Y . Pan, H. Xie, Y . Zhang, T. Yao, and T. Mei, “Hifi3d: Improving text-to-3d with high-fidelity multi-view diffusion,” IEEE Transactions on Multimedia, 2026

    2026

  12. [20]

    Difftf++: 3d-aware diffu- sion transformer for large-vocabulary 3d generation,

    Z. Cao, F. Hong, T. Wu, L. Pan, and Z. Liu, “Difftf++: 3d-aware diffu- sion transformer for large-vocabulary 3d generation,”IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025

    2025

  13. [21]

    Disr-nerf: Diffusion-guided view- consistent super-resolution nerf,

    J. L. Lee, C. Li, and G. H. Lee, “Disr-nerf: Diffusion-guided view- consistent super-resolution nerf,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 20 561–20 570

    2024

  14. [22]

    Dreamgaussian: Generative gaussian splatting for efficient 3d content creation,

    J. Tang, J. Ren, H. Zhou, Z. Liu, and G. Zeng, “Dreamgaussian: Generative gaussian splatting for efficient 3d content creation,”arXiv preprint arXiv:2309.16653, 2023

    2023 arXiv

  15. [23]

    Diffusion-based facial aesthetics enhancement with 3d structure guidance,

    L. Li, J. Hou, W. Liu, Y . Fang, and J. Yan, “Diffusion-based facial aesthetics enhancement with 3d structure guidance,”IEEE Transactions on Image Processing, vol. 34, pp. 1879–1894, 2025

    2025

  16. [24]

    Clay: A controllable large-scale generative model for creating high-quality 3d assets,

    L. Zhang, Z. Wang, Q. Zhang, Q. Qiu, A. Pang, H. Jiang, W. Yang, L. Xu, and J. Yu, “Clay: A controllable large-scale generative model for creating high-quality 3d assets,”ACM Transactions on Graphics (TOG), vol. 43, no. 4, pp. 1–20, 2024

    2024

  17. [25]

    Structured 3d latents for scalable and versatile 3d generation,

    J. Xiang, Z. Lv, S. Xu, Y . Deng, R. Wang, B. Zhang, D. Chen, X. Tong, and J. Yang, “Structured 3d latents for scalable and versatile 3d generation,” inProceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2025, pp. 21 469–21 480

    2025

  18. [26]

    Realfusion: 360deg reconstruction of any object from a single image,

    L. Melas-Kyriazi, I. Laina, C. Rupprecht, and A. Vedaldi, “Realfusion: 360deg reconstruction of any object from a single image,” inIEEE/CVF conference on computer vision and pattern recognition, 2023, pp. 8446– 8455

    2023

  19. [27]

    Latent-nerf for shape-guided generation of 3d shapes and textures,

    G. Metzer, E. Richardson, O. Patashnik, R. Giryes, and D. Cohen-Or, “Latent-nerf for shape-guided generation of 3d shapes and textures,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 12 663–12 673

    2023

  20. [28]

    One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion,

    M. Liu, R. Shi, L. Chen, Z. Zhang, C. Xu, X. Wei, H. Chen, C. Zeng, J. Gu, and H. Su, “One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 10 072–10 083

    2024

  21. [29]

    Multimodal industrial anomaly detection via hybrid fusion,

    Y . Wang, J. Peng, J. Zhang, R. Yi, Y . Wang, and C. Wang, “Multimodal industrial anomaly detection via hybrid fusion,” inIEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, 2023, pp. 8032–8041

    2023

  22. [30]

    Duinnet: Dual-modality feature interaction for point cloud completion,

    X. Liu, B. Hou, H. Wang, K. Xu, J. Wan, and Y . Guo, “Duinnet: Dual-modality feature interaction for point cloud completion,”IEEE Transactions on Multimedia, 2025

    2025

  23. [31]

    Shape-guided dual-memory learning for 3d anomaly detection,

    Y .-M. Chu, C. Liu, T.-I. Hsieh, H.-T. Chen, and T.-L. Liu, “Shape-guided dual-memory learning for 3d anomaly detection,” inProceedings of the 40th International Conference on Machine Learning, 2023, pp. 6185– 6194

    2023

  24. [32]

    Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection,

    E. Horwitz and Y . Hoshen, “Back to the feature: classical 3d features are (almost) all you need for 3d anomaly detection,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 2968–2977

    2023

  25. [33]

    3d gaussian splatting for real-time radiance field rendering

    B. Kerbl, G. Kopanas, T. Leimk ¨uhler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering.”ACM Trans. Graph., vol. 42, no. 4, pp. 139–1, 2023

    2023

  26. [34]

    Cutpaste: Self-supervised learning for anomaly detection and localization,

    C.-L. Li, K. Sohn, J. Yoon, and T. Pfister, “Cutpaste: Self-supervised learning for anomaly detection and localization,” inIEEE/CVF confer- ence on computer vision and pattern recognition, 2021, pp. 9664–9674

    2021

  27. [35]

    Bridging 3d anomaly localization and repair via high-quality con- tinuous geometric representation,

    B. Zheng, J. Gan, X. Xu, X. Chen, W. Li, X. Huang, and Y . Wu, “Bridging 3d anomaly localization and repair via high-quality con- tinuous geometric representation,” inProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 27 063– 27 072

    2025

  28. [36]

    Mc3d-ad: A unified geometry-aware reconstruction model for multi-category 3d anomaly detection,

    J. Cheng, C. Gao, J. Zhou, J. Wen, T. Dai, and J. Wang, “Mc3d-ad: A unified geometry-aware reconstruction model for multi-category 3d anomaly detection,”arXiv preprint arXiv:2505.01969, 2025

    2025

  29. [37]

    Towards total recall in industrial anomaly detection,

    K. Roth, L. Pemula, J. Zepeda, B. Sch ¨olkopf, T. Brox, and P. Gehler, “Towards total recall in industrial anomaly detection,” inIEEE/CVF conference on computer vision and pattern recognition, 2022, pp. 14 318–14 328

    2022

  30. [38]

    Registration based few-shot anomaly detection,

    C. Huang, H. Guan, A. Jiang, Y . Zhang, M. Spratling, and Y .-F. Wang, “Registration based few-shot anomaly detection,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 303–319

    2022

  31. [39]

    Meshanything: Artist-created mesh generation with autoregressive transformers,

    Y . Chen, T. He, D. Huang, W. Ye, S. Chen, J. Tang, X. Chen, Z. Cai, L. Yang, G. Yuet al., “Meshanything: Artist-created mesh generation with autoregressive transformers,”arXiv preprint arXiv:2406.10163, 2024

    2024

  32. [40]

    Registration is a powerful rotation-invariance learner for 3d anomaly detection,

    Y . Yu, Z. Chen, X. Xu, L. Zhang, H. Yang, Y . Nie, and S. He, “Registration is a powerful rotation-invariance learner for 3d anomaly detection,”arXiv preprint arXiv:2510.16865, 2025

    2025

  33. [41]

    Efficient simplification of point- sampled surfaces,

    M. Pauly, M. Gross, and L. P. Kobbelt, “Efficient simplification of point- sampled surfaces,” inIEEE Visualization, 2002. VIS 2002.IEEE, 2002, pp. 163–170

    2002

Pith tools

Reviewed June 25, 2026 · model on record in the stance chip above.