A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

Cheng Jiang; Haiming Yao; Hui Zhang; Wei Luo; Weiming Shen; Yunkang Cao; Yuqi Cheng

REVIEW 3 major objections 6 minor 1 cited by

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This survey claims to be the first systematic account of industrial defect detection's transition from closed-set to open-set methods across both 2D and 3D data.

desk verdict Useful 2D/3D survey with an overstated 'first' claim and no documented selection methodology; worth citing with caveats, but needs revision before it can be the field's reference. read the letter →

arxiv 2507.13378 v1 pith:IHQHSXOV submitted 2025-07-15 cs.CV

Yuqi Cheng , Yunkang Cao , Haiming Yao , Wei Luo , Cheng Jiang , Hui Zhang , Weiming Shen This is my paper

classification cs.CV

keywords industrialdefectdetectionanomalyclosed-setopen-set2Dand3Dmodalitiespointcloudinspectiondeeplearningsurveymulti-modal

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 survey argues that industrial defect detection has undergone a fundamental shift: older closed-set systems, trained on labeled examples of every defect category they will ever see, are giving way to open-set systems that flag anomalies never encountered in training. It claims to be the first review to examine this transition systematically across both 2D image-based and 3D point-cloud-based inspection. The main contribution is an organizing taxonomy that pairs the closed-set/open-set distinction with data modality, together with a synthesis of recent methods, datasets, and open challenges. If the survey's account is right, the field's center of gravity is moving toward detection pipelines that need few or no defect annotations.

What carries the argument

The load-bearing organization is a two-axis taxonomy: closed-set versus open-set detection crossed with 2D versus 3D data. Closed-set means supervised recognition of predefined defect classes; open-set means identifying previously unseen anomalies, typically by modeling normality. The taxonomy does the argument's work by turning a scattered method literature into a single narrative: as industrial production shifts to small batches and varied products, defect annotations become scarce, and the field pivots from closed-set to open-set, with 3D and multi-modal methods added to catch defects invisible to 2D texture analysis.

What would settle it

A reproducible, documented literature search that turns up an earlier survey covering the same closed-set-to-open-set span across 2D and 3D would refute the 'first' claim; re-reading a random sample of the cited papers for mischaracterization would test the accuracy premise.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that a single conceptual axis, closed-set versus open-set defect detection, now structures how the field should be understood. Closed-set methods assume all defect classes are known at training time and rely on supervised object detection or segmentation; open-set methods, often called industrial anomaly detection, learn normality from defect-free data and flag deviations, which removes the need for extensive anomaly annotations. The survey maps this axis onto two modalities: 2D detection, which captures textural defects through RGB and multi-spectral imaging, and 3D detection, which captures geometric defects through point clouds, depth sensors, and reference-model registration. It subdivides open-set 2D methods into unsupervised, semi-supervised, and zero/few-shot families, and 3D open-set methods into reference-based, point-cloud-based, and multi-modal-based approaches. It also compiles a table of public benchmarks and identifies future directions toward unified, controllable, configurable, high-resolution, and explainable systems.

Load-bearing premise

The load-bearing premise is that the set of papers this survey chose to review is representative of the field and is characterized accurately, since the survey does not document its literature search or inclusion criteria.

Editorial extensions

If this is right

Open-set detection, rather than further refinement of closed-set detection, is the direction the survey sees as dominant, so research effort and benchmark design should follow that trend.
Because open-set methods learn from normal samples, adopting them reduces dependence on costly defect annotations, which the survey identifies as a major industrial bottleneck.
The survey's dataset table gives practitioners a single entry point for choosing benchmarks by modality, task type, and realism, including recent multi-view and multi-illumination sets.
From the survey's analysis, a unified framework that can slide between closed-set and open-set regimes as defect data accumulate is a concrete next target.
For 3D inspection, reference-based methods work only when high-fidelity CAD models and accurate registration are available, so point-cloud and multi-modal methods are the more robust open-set route.

Reading between the lines

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

Editorial: if the taxonomy becomes standard, benchmark suites could be redesigned to test the closed-set-to-open-set continuum explicitly, for instance evaluating one model as labeled defect data accumulate over a product's life cycle.
Editorial: the 'first systematic examination' claim is time-sensitive; an earlier survey with the same scope would undercut it, which makes the absence of a documented search protocol worth checking.
Editorial: the survey's own distinction suggests a testable prediction: open-set methods that combine 2D texture cues with 3D geometry should outperform single-modality methods on defects that are subtle in both modalities, such as faint dents on reflective surfaces.
Editorial: if controllable defect generation matures, the closed-set/open-set boundary itself may blur, because synthetic anomalies could populate training sets that were previously annotation-starved.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this is a useful survey that overstates its novelty. The 2D/3D organization, the dataset table, and the timelines give a newcomer a decent map of the field, including 2024-2025 work. The method descriptions are mostly consistent with what I know. If you need a quick orientation to industrial defect detection across both image and point-cloud modalities, this is a reasonable starting point.\n\nThe main problem is the claim in Section 1 that this is 'the first to systematically examine the transition from closed-set to open-set DD, synthesizing insights across both 2D and 3D modalities.' That is not established. Reference [10] is a 2025 Information Fusion survey explicitly covering RGB, 3D, and multimodal unsupervised anomaly detection, which overlaps heavily with the claimed scope. The authors' own group also has an earlier visual anomaly detection survey [3]. To make the claim stick they need to explain what [10] covers and what this survey adds, task-wise and modality-wise. Right now the differentiation is just an assertion.\n\nThe second soft spot is methodology. There is no description of how the 322 references were selected, no search strategy, no inclusion/exclusion criteria. For a survey that stakes its value on comprehensiveness, that is a real gap. The heavy presence of the authors' own work among the references doesn't by itself invalidate anything, but it makes the lack of a documented selection process more consequential.\n\nMinor issues: the dataset table labels DAGM 2007 as 'Close-Set,' which is okay if you mean supervised classification, but that dataset isn't usually described that way; and Section 3.2.4 calls AnomalyAnything 'a notable first in the field' without support. Both are small.\n\nOn balance, the internal synthesis is coherent and I don't see a load-bearing flaw in the descriptions. The paper is most useful as a reference document for practitioners and new researchers, not as a research contribution. It deserves peer review, but the 'first' claim and the missing methodology must be addressed in revision. I'd cite it with caveats; I'd bring it to a reading group mostly to debate the novelty claim.

Referee Report

3 major / 6 minor

Summary. This manuscript presents a survey of industrial defect detection (DD) spanning 2D and 3D modalities and organized around the transition from closed-set to open-set paradigms. It introduces a generic DD system framework, compiles 23 public datasets in Table 3, reviews close-set 2D and 3D methods, and then surveys unsupervised, semi-supervised, few-shot, zero-shot, VLM/MLLM-based open-set methods for 2D and reference-based, point-cloud-based, and multimodal methods for 3D. The paper closes with future directions toward unified frameworks, controllable defect generation, configurable systems, larger datasets, high-resolution detection, high-semantic detection, and explainability. The central contribution claimed is being the first survey to systematically examine the closed-set-to-open-set transition across both 2D and 3D modalities.

Significance. If the coverage and novelty claims are substantiated, this survey would be a valuable reference for the industrial anomaly detection community. The organization around closed-set versus open-set is a sensible lens, and Table 3's dataset compilation is genuinely useful. The taxonomy of 2D open-set methods (regression-, memory-bank-, flow-, discrimination-, semi-supervised, zero/few-shot, VLM/MLLM-based) is broad and mostly consistent with the literature, and the 3D section usefully separates reference-based, point-cloud-based, and multimodal approaches. However, the survey's value as a 'comprehensive' reference depends on the representativeness and accuracy of its 322 cited works, and on a sharp differentiation from prior surveys, neither of which is currently documented.

major comments (3)

[Section 1] The central claim that this is 'the first to systematically examine the transition from closed-set to open-set DD, synthesizing insights across both 2D and 3D modalities' is not adequately differentiated from prior survey [10] (Lin et al., Information Fusion 2025), whose declared scope already covers RGB, 3D, and multimodal unsupervised anomaly detection. The manuscript dismisses [10] in a list of sub-field-specific reviews without specifying what [10] actually covers or what the present survey adds beyond it. Please provide an explicit comparison of scope, taxonomy, and covered tasks that supports the 'first' claim.
[Section 1 (methodology)] Nowhere in the paper is the literature selection process documented: there is no search strategy, database list, time span, inclusion/exclusion criteria, or quality assessment protocol. For a survey whose stated value is comprehensiveness and whose method and dataset coverage are drawn from 322 references, this omission makes the representativeness claim unverifiable. A methodology paragraph should be added (e.g., at the end of Section 1) before the 'comprehensive' claim can be assessed.
[Section 4.1] Section 4.1 ('Close-Set 3D Defect Detection') does not actually survey defect-detection-specific methods: most of the subsection enumerates general point cloud architectures (PointNet [222], PointNet++ [223], RandLA-Net [224], DGCNN [233], Point Transformer [237], etc.) without an industrial defect dataset, task formulation, or evaluation that ties them to DD. If these are intended as background backbone material, the text should say so explicitly; otherwise the survey's coverage of close-set 3D DD is materially overstated and should be rewritten around works that perform defect detection on point clouds.

minor comments (6)

[Section 3.2.4] The method name 'AnomalyAny [220]' does not match the cited reference, which is titled 'Anomaly Anything: Promptable Unseen Visual Anomaly Generation.' Please use a consistent name, and either support the phrase 'a notable first in the field' with comparison to prior generation works (e.g., AnomalyDiffusion, Defect-Gen, AnomalyPainter) or soften it.
[Section 3.1] In 'Addressing Subtle Defects,' the method named 'DWWA-Net [47]' does not match reference [47], which is titled 'DMPDD-Net: An effective defect detection method for aluminum profiles surface defect.' Please correct the citation or the method name.
[References] References [28] and [101] appear to be duplicate entries for the same ECCV 2024 paper ('A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization'). Please merge and renumber.
[Section 2.2] The sentence 'More recently, the adoption of multimodal data—including RGB images, point clouds, and infrared imagery [20])—has gained prominence' contains an unbalanced closing parenthesis after '[20]'.
[Section 4.1.2] The sentence 'OctNet [252] and SS-CNs [253] exploits sparsity' should read 'OctNet [252] and SS-CNs [253] exploit sparsity.'
[Section 3.2.3] The statement that 'WinCLIP [80] pioneers zero-shot anomaly detection' is internally inconsistent with the earlier 'Vanilla Zero-shot Anomaly Detection' paragraph, which already describes zero-shot methods such as Aota et al. [185]. Please rephrase to 'pioneers VLM-based zero-shot anomaly detection.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the survey's synthesis is independent of its self-citations; the novelty claim is a factual assertion, not a derivation.

full rationale

This is a survey, not a derivation chain, so most circularity patterns do not apply. The paper's taxonomy (closed-set vs. open-set, 2D vs. 3D) is an organizing scheme applied to independently published methods; it does not fit any parameter to data and then rename the fit as a prediction. Equation (1) states the standard ICP registration objective and cites Besl & McKay [256], an appropriate external source; no result is derived from the equation. The 'first survey' claim is a novelty assertion supported by the paper's characterization of prior reviews [9,10,11,1,12,2]; even if that characterization is contestable (e.g., ref. [10] covers RGB/3D/multimodal unsupervised anomaly detection), a disputed novelty claim is a factual correctness issue, not circularity. The authors' self-citations (e.g., [3,7,13,79,81,117,129,191,209,220,285,287,291]) point to specific methods and datasets, but none is the sole justification for the survey's organizing claims, and the surveyed methods are also independently published and externally described. The absence of documented literature-search criteria weakens the comprehensiveness claim as a transparency matter, but it is not a circular reduction. No load-bearing step reduces by construction to its own input, and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

As a literature review, the survey introduces no free parameters or invented entities. It depends on two domain assumptions: the choice of organizing axes and the representativeness/accuracy of the selected literature.

assumptions (2)

domain assumption The closed-set vs open-set axis and the 2D vs 3D modality split are the organizing principles for defect detection.
The entire survey structure and the claimed synthesis rest on this categorization. If another axis, such as supervision level or task granularity, were primary, the survey would be organized differently. This is stated in the abstract and Section 1.
domain assumption The cited papers are accurately characterized and the selection of papers is representative of the field.
The survey provides no explicit inclusion/exclusion criteria or systematic search protocol, so the comprehensiveness claim relies on the authors' choices. This is a load-bearing assumption for a survey claiming to be comprehensive.

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Cite this review

Pith. "Pith review of A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects." pith.science (2026). https://pith.science/paper/IHQHSXOV

@misc{pith2026250713378,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IHQHSXOV}},
  note         = {Machine review of arXiv:2507.13378}
}

read the original abstract

Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.

Figures

Figures reproduced from arXiv: 2507.13378 by the authors.

**Figure 1.** Illustration of 2D and 3D defect detection paradigms. Left: 2D detection operates on RGB images to localize surface defects, exemplified using samples from 𝑀2AD [7]. Right: 3D detection leverages geometric representations such as point clouds, with data from MiniShift [13]. Both approaches aim for precise defect localization, as visualized in the bottom row. modalities. We pledge to periodically update this work to … view at source ↗

**Figure 2.** Organization of this survey. 2.2. Defect Detection Datasets Public datasets have emerged as vital tools for validating and refining algorithmic performance, significantly expediting the evolution of defect detection models [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Overview of a typical industrial defect detection system. The system consists of three main components: (1) Detection Equipment, including mobile platforms (e.g., conveyor belts, robotic arms), light sources (e.g., ring and bar lights), and various sensors (e.g., RGB cameras, depth sensors, laser scanners); (2) Data Processing and Analysis System, where multi-view data are collected to construct multimodal datasets… view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: Schematic of close-set and open-set DD paradigms. (a) Close-set detection, assuming all defect types are known at training. (b) Unsupervised open-set detection, trained solely on normal samples. (c) Semi-supervised open-set detection, leveraging a limited number of ano…

**Figure 5.** Figure 5: Timeline of representative methodologies in 2D DD. In recent years, there has been a growing interest in open-set DD, accompanied by the emergence of various subtasks such as semi-supervised DD and zero-/few-shot DD. superior generalization and scalability across diver…

**Figure 6.** Figure 6: Timeline of representative methodologies in 3D DD. previous 2D defect detection methods. The representative methods are shown in [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

**Figure 7.** Figure 7: Representative schemes for 3D open-set DD. (a) Reference-based anomaly detection leverages comparisons with CAD models; (b) Point cloud-based anomaly detection operates directly on 3D geometry to identify irregularities; (c) Multi-modal-based anomaly detection integrat…

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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.

Multi-View Reconstruction with Global Context for 3D Anomaly Detection
cs.CV 2025-07 conditional novelty 5.0 of 10

MVR projects high-resolution point clouds into multi-view depth images and reconstructs them with a pre-trained vision transformer, achieving state-of-the-art anomaly detection scores on Real3D-AD.

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