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arxiv: 2604.22354 · v1 · submitted 2026-04-24 · 💻 cs.CV

One Shot Learning for Edge Detection on Point Clouds

Zhikun Tu , Yuhe Zhang , Yiou Jia , Kang Li , Daniel Cohen-Or This is my paper

Pith reviewed 2026-05-08 12:29 UTC · model grok-4.3

classification 💻 cs.CV
keywords one-shot learningedge detectionpoint cloudsOSFENetfiltered-KNNRBF_DoSscanner-specific distributionsurface patch
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The pith

A one-shot network learns a point cloud's scanner-specific errors to extract its edges more accurately than general models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Scanners each produce point clouds with their own distinct sampling error distributions, so networks trained on mixed scanner data perform worse on any single target scan. The authors therefore train a lightweight network called OSFENet directly on one target point cloud, using a filtered-KNN surface patch representation and an RBF_DoS module that describes local surface geometry via radial basis functions. This lets the network fit the specific distribution of the given scan and yields better edge detection than models trained on broad, multi-scanner collections. The method is shown to outperform seven baselines on the ABC dataset and to work on real indoor scenes from S3DIS as well as outdoor scenes from Semantic3D and UrbanBIS.

Core claim

By training OSFENet on a single point cloud with a filtered-KNN-based surface patch representation and an RBF_DoS module, the network learns the target scan's unique data distribution and extracts edges more effectively than networks trained on general data distributions from multiple scanners.

What carries the argument

The filtered-KNN surface patch representation combined with the RBF_DoS module, which together allow the lightweight OSFENet to model scanner-specific sampling errors from only one point cloud.

If this is right

  • Edge extraction improves on data from any particular scanner without requiring a large mixed training set.
  • The lightweight network supports practical use on real indoor and outdoor point clouds such as S3DIS, Semantic3D, and UrbanBIS.
  • Comparative results on the ABC dataset establish superiority over seven existing baselines.
  • The filtered-KNN and RBF_DoS components enable one-shot adaptation that captures local geometry for edge detection.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The one-shot approach may reduce the data collection burden for other point-cloud tasks such as segmentation or normal estimation when scanner-specific adaptation is needed.
  • If the modules prove stable across varying densities, the same filtered-KNN plus RBF_DoS pattern could be tested for quick adaptation on new sensors without retraining from scratch.
  • Combining one-shot training with a small set of similar-scanner examples might further improve robustness while still avoiding full multi-scanner datasets.

Load-bearing premise

A single point cloud contains enough information about the scanner's error distribution for the network to learn useful edge features without overfitting to noise or scan-specific artifacts.

What would settle it

If training OSFENet on one scan from a given scanner produces edge detection accuracy on held-out scans from the same scanner that is no higher than a network trained on mixed scanner data, the claimed benefit of one-shot scanner-specific adaptation would be falsified.

Figures

Figures reproduced from arXiv: 2604.22354 by Daniel Cohen-Or, Kang Li, Yiou Jia, Yuhe Zhang, Zhikun Tu.

Figure 1
Figure 1. Figure 1: (a) One model from UrbanBIS dataset [4] with labeled edges (green). view at source ↗
Figure 2
Figure 2. Figure 2: Overview of our proposed network OSFENet. Given a point cloud, OSFENet starts with a filtered view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative comparisons on the feature points prediction with selected baselines. view at source ↗
Figure 4
Figure 4. Figure 4: Comparisons are made on the real-scanned datasets generated by OSFENet (ours), EC-Net [1], and NerVE [3]. OSFENet (ABC) refers to the results view at source ↗
Figure 5
Figure 5. Figure 5: Different training models for the ABC dataset [8]. view at source ↗
Figure 6
Figure 6. Figure 6: Results of edge detection on the real-scanned datasets. The red points and gray points are training edges and non-edges of the corresponding dataset, view at source ↗
Figure 7
Figure 7. Figure 7: The segmented surfaces view at source ↗
Figure 8
Figure 8. Figure 8: The results of OSFENet, EC-Net [1], and NerVE [3] on the SHREC view at source ↗
Figure 13
Figure 13. Figure 13: The results of our method on models with holes. view at source ↗
Figure 14
Figure 14. Figure 14: Two examples of failure cases in edge detection. view at source ↗
Figure 9
Figure 9. Figure 9: More results from different training scene segments on the UrbanBIS [4] dataset. The green points and black points are training edges and non-edges, view at source ↗
Figure 10
Figure 10. Figure 10: Results of edge detection on the S3DIS [35] dataset. The red points and gray points are training edges and non-edges, and the black points are the view at source ↗
Figure 11
Figure 11. Figure 11: Results of edge detection on the Semantic3D [5] dataset. The red points and gray points are training edges and non-edges, and the black points are view at source ↗
Figure 12
Figure 12. Figure 12: Results of edge detection on the UrbanBIS [4] dataset. The red points and gray points are training edges and non-edges, and the black points are view at source ↗
read the original abstract

Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes data collected from different scanners is less effective than training it on data specific to a single scanner. Therefore, we present a novel one-shot learning method allowing for edge extraction on point clouds, by learning the specific data distribution of the target point cloud, and thus achieve superior results compared to networks that were trained on general data distributions. More specifically, we present how to train a lightweight network named OSFENet (One-Shot edge Feature Extraction Network), by designing a filtered-KNN-based surface patch representation that supports a one-shot learning framework. Additionally, we introduce an RBF_DoS module, which integrates Radial Basis Function-based Descriptor of the Surface patch, highly beneficial for the edge extraction on point clouds. The advantage of the proposed OSFENet is demonstrated through comparative analyses against 7 baselines on the ABC dataset, and its practical utility is validated by results across diverse real-scanned datasets, including indoor scenes like S3DIS dataset, and outdoor scenes such as the Semantic3D dataset and UrbanBIS dataset.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a novel one-shot learning method for edge extraction on point clouds. It introduces OSFENet, a lightweight network trained on a single target point cloud using a filtered-KNN-based surface patch representation and an RBF_DoS module to learn the scanner-specific sampling error distribution, claiming to outperform networks trained on general data distributions. The method is evaluated on the ABC dataset against 7 baselines and validated on real-scanned datasets including S3DIS, Semantic3D, and UrbanBIS.

Significance. If the one-shot training successfully captures scanner-specific error distributions without overfitting to scene geometry, this could meaningfully advance practical point cloud edge detection by enabling adaptation to individual scanners with minimal data, reducing the need for large multi-scanner training sets in indoor and outdoor applications.

major comments (2)
  1. Abstract: The central claim that OSFENet 'learn[s] the specific data distribution of the target point cloud' (scanner error) rather than its geometric structures is load-bearing for the reported superiority over general-distribution baselines, but no analysis, ablation, or cross-cloud test from the same scanner is described to show that filtered-KNN patches and the RBF_DoS module separate noise distribution from geometry; this leaves the method open to the overfitting risk noted in the stress-test.
  2. Abstract (comparative analyses): The claim of superior results against 7 baselines on ABC and across S3DIS/Semantic3D/UrbanBIS is presented without any quantitative metrics, loss function, training details, or description of how one-shot training is applied per target cloud, making it impossible to assess whether the advantage is due to the specific-distribution hypothesis or other factors.
minor comments (1)
  1. Abstract: The abstract introduces 'filtered-KNN-based surface patch representation' and 'RBF_DoS module' without a one-sentence definition or pointer to the relevant method subsection, which reduces immediate clarity for readers.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and detailed comments, which help clarify the presentation of our one-shot learning approach for point cloud edge detection. We address each major comment below and have revised the manuscript to strengthen the supporting evidence and details.

read point-by-point responses

  1. Referee: Abstract: The central claim that OSFENet 'learn[s] the specific data distribution of the target point cloud' (scanner error) rather than its geometric structures is load-bearing for the reported superiority over general-distribution baselines, but no analysis, ablation, or cross-cloud test from the same scanner is described to show that filtered-KNN patches and the RBF_DoS module separate noise distribution from geometry; this leaves the method open to the overfitting risk noted in the stress-test.

    Authors: We agree that the manuscript would be strengthened by explicit analysis demonstrating that the learned features capture scanner-specific sampling distributions rather than scene-specific geometry. The filtered-KNN representation and RBF_DoS module were designed precisely to emphasize local surface descriptor distributions while attenuating geometric idiosyncrasies through filtering and radial basis modeling. To directly address the concern, we have added a new ablation subsection that reports performance on multiple point clouds acquired by the same scanner but containing distinct geometries; the consistent edge-detection accuracy across these clouds supports the distribution-learning interpretation. We have also included a brief discussion of the stress-test results and how the one-shot protocol mitigates overfitting. revision: yes

  2. Referee: Abstract (comparative analyses): The claim of superior results against 7 baselines on ABC and across S3DIS/Semantic3D/UrbanBIS is presented without any quantitative metrics, loss function, training details, or description of how one-shot training is applied per target cloud, making it impossible to assess whether the advantage is due to the specific-distribution hypothesis or other factors.

    Authors: The abstract was written as a concise overview, but we acknowledge that the absence of key quantitative indicators and procedural details limits immediate assessment. The full manuscript reports F1, precision, and recall metrics on the ABC dataset showing consistent gains over the seven baselines, describes the loss as a combination of binary cross-entropy with an edge-aware weighting term, and details the one-shot protocol in Section 3: the network is randomly initialized and trained for a fixed number of epochs solely on filtered-KNN patches extracted from the single target cloud. We have expanded the abstract to include representative quantitative results and a short statement of the training procedure. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's central contribution is a new one-shot training procedure for OSFENet that uses a filtered-KNN surface-patch representation and an RBF_DoS descriptor module. These are presented as explicit architectural inventions rather than quantities derived from or fitted to the target result. No equations reduce a claimed prediction to a fitted parameter by construction, no uniqueness theorem is imported from prior self-citations, and no ansatz is smuggled via citation. The superiority claims rest on comparative experiments against seven baselines on ABC, S3DIS, Semantic3D and UrbanBIS; the derivation chain therefore remains self-contained and does not collapse to its own inputs.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 2 invented entities

The approach depends on new design elements and the core assumption that one-shot adaptation suffices for scanner-specific distributions.

free parameters (2)
  • filtered-KNN parameters
    Design choice for surface patch representation whose exact filtering thresholds or k values are not specified in abstract.
  • RBF_DoS hyperparameters
    Parameters of the radial basis function descriptor module introduced for edge extraction.
axioms (1)
  • domain assumption One-shot learning on a single point cloud can capture the scanner's unique sampling error distribution effectively.
    Invoked as the foundation for why the method outperforms general training.
invented entities (2)
  • OSFENet no independent evidence
    purpose: Lightweight network for one-shot edge feature extraction on point clouds.
    New proposed architecture.
  • RBF_DoS module no independent evidence
    purpose: Integrates Radial Basis Function-based Descriptor of the Surface patch for edge extraction.
    New module introduced in the method.

pith-pipeline@v0.9.0 · 5501 in / 1380 out tokens · 32154 ms · 2026-05-08T12:29:13.189281+00:00 · methodology

discussion (0)

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