Paper Citation Record · LEDGER

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection

As of 20 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2504.14221.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2504.14221 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:57:04.648930Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T14:16:40.509571Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T14:20:30.650543Z

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy25
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fd209c50-0705-467e-83c0-47f80c721722 · outbound

This paper cites Anomaly detection in 3d point clouds using deep geometric descriptors.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Anomaly detection in 3d point clouds using deep geometric descriptors

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.985046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c81ecb64-9fd5-4441-a934-e16d7bdb5cad · outbound

This paper cites Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Mvtec ad–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.974684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 804d35ec-a72b-41a8-b93a-c905c42c1461 · outbound

This paper cites Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.545514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8c7324d-c275-4f53-b75c-85aa9bf04712 · outbound

This paper cites The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.548791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4083b34c-ceef-43a4-871b-d60280a970ce · outbound

This paper cites The eyecandies dataset for unsupervised multimodal anomaly detection and local- ization.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection The eyecandies dataset for unsupervised multimodal anomaly detection and local- ization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.958293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 52238573-fb9e-4ff6-8686-4a4b1561f796 · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.556285Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c4a0783-833e-4df8-a028-6e78011b94f7 · outbound

This paper cites Easynet: An efficient network for 3d industrial anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Easynet: An efficient network for 3d industrial anomaly detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.947897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 354d3d3f-9c99-40ed-931e-314d5f0fe000 · outbound

This paper cites APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection APRIL-GAN: A Zero-/Few-Shot Anomaly Classification and Segmentation Method for CVPR 2023 VAND Workshop Challenge Tracks 1&2: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.563584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e9303f5-b0b3-4edd-a67e-c06a8a8d6973 · outbound

This paper cites Shape-guided dual-memory learning for 3d anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Shape-guided dual-memory learning for 3d anomaly detection

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.938266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7b5173c9-b44a-43ad-8e94-0421a414b3fc · outbound

This paper cites Padim: a patch distribution modeling framework for anomaly detection and localization.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Padim: a patch distribution modeling framework for anomaly detection and localization

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.928461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8f8bd9f5-9771-4e77-b301-01a67d99c62b · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Anomaly detection via reverse distillation from one-class embedding

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.918170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5be17c04-3322-4690-a0bd-9ac311e221a1 · outbound

This paper cites Cflow-ad: Real-time unsupervised anomaly detection with compact neural flow.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Cflow-ad: Real-time unsupervised anomaly detection with compact neural flow

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.908494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 489c2e73-8bc1-4845-998c-f792c1b3aeb0 · outbound

This paper cites A diffusion-based framework for multi-class anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection A diffusion-based framework for multi-class anomaly detection

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.580275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0412a5ae-3a23-4879-bbe9-210c9a25dad1 · outbound

This paper cites Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Learning Unified Reference Representation for Unsupervised Multi-class Anomaly Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.583140Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd68beb3-343e-4d91-9a7b-21dded9a8489 · outbound

This paper cites Back to the feature: clas- sical 3d features are (almost) all you need for 3d anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Back to the feature: clas- sical 3d features are (almost) all you need for 3d anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.893120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c87d7085-0d34-4dcd-ac08-1265b9523be3 · outbound

This paper cites Winclip: Zero- /few-shot anomaly classification and segmentation.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Winclip: Zero- /few-shot anomaly classification and segmentation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.589305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f760ed24-ad03-47ae-8231-c3916ee9db00 · outbound

This paper cites Pyramid- flow: High-resolution defect contrastive localization using pyramid normalizing flow.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Pyramid- flow: High-resolution defect contrastive localization using pyramid normalizing flow

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.878578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d45cb0ca-f7b2-4375-9204-61a01bc53620 · outbound

This paper cites Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Promptad: Learn- ing prompts with only normal samples for few-shot anomaly detection

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.869453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e1f3cabf-10dc-4a6b-a8e8-d6da903ad91b · outbound

This paper cites Real3d- ad: A dataset of point cloud anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Real3d- ad: A dataset of point cloud anomaly detection

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.860072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6939ca3e-c599-427c-969d-597e6bf83434 · outbound

This paper cites Model- ing the distribution of normal data in pre-trained deep fea- tures for anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Model- ing the distribution of normal data in pre-trained deep fea- tures for anomaly detection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.601537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3594eaa4-28f5-42e0-b4df-fdf85a0e041f · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Towards to- tal recall in industrial anomaly detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.844093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 95b0d725-0777-4985-a27a-1870305d2fad · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Towards to- tal recall in industrial anomaly detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.834678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05256545-7cec-415a-bc43-19ce305feeec · outbound

This paper cites Asymmetric student-teacher networks for indus- trial anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Asymmetric student-teacher networks for indus- trial anomaly detection

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.824649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8b0289b5-759b-4386-982c-3f5ceb077030 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Multiresolution knowledge distillation for anomaly detection

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.815253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a49c6735-7bf5-4386-b333-6c3b44c16364 · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Revisiting reverse distillation for anomaly detection

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.805488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 25e03773-6e5c-4b78-98e5-be48603a1482 · outbound

This paper cites Position encoding enhanced feature mapping for image anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Position encoding enhanced feature mapping for image anomaly detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.796048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6b81a8f9-1b2c-4f10-9049-aa4b3c48fb10 · outbound

This paper cites Pspu: Enhanced positive and unlabeled learning by leveraging pseudo super- vision.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Pspu: Enhanced positive and unlabeled learning by leveraging pseudo super- vision

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.786776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 50d7c3b6-5b7d-41ac-bc7f-8e6d4f0b54f8 · outbound

This paper cites Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Real-iad: A real-world multi-view dataset for benchmarking versatile industrial anomaly detec- tion

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-16T11:57:04.625905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ac836dec-47e3-421c-8fec-a3a0a37ba894 · outbound

This paper cites Softpatch+: Fully un- supervised anomaly classification and segmentation.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Softpatch+: Fully un- supervised anomaly classification and segmentation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.772192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eb10d4e2-4367-4bcd-a134-f699f09a38a8 · outbound

This paper cites Multimodal industrial anomaly detection via hybrid fusion.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Multimodal industrial anomaly detection via hybrid fusion

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.761075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 86ece97b-1a23-4f94-8fb1-147d13a99dc5 · outbound

This paper cites Pushing the limits of fewshot anomaly detection in industry vision: Graphcore.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Pushing the limits of fewshot anomaly detection in industry vision: Graphcore

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.751657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b0e0a43c-8164-4dfa-aa4b-2a1515c202f4 · outbound

This paper cites A unified model for multi-class anomaly detection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection A unified model for multi-class anomaly detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.742563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ae25640f-6b0f-4166-99f8-e424f7782f05 · outbound

This paper cites Cheat- ing depth: Enhancing 3d surface anomaly detection via depth simulation.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Cheat- ing depth: Enhancing 3d surface anomaly detection via depth simulation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.732598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5d75b279-a49e-4110-a5c7-95d9fcafa553 · outbound

This paper cites an unresolved cited work.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Unresolved cited work

Reference 34

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:57:04.723004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 51f7184f-257d-43ca-a837-f36e66cb26ce · outbound

This paper cites Spot-the- difference: A novel benchmark for image anomaly detection in industrial inspection.

Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Spot-the- difference: A novel benchmark for image anomaly detection in industrial inspection

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:57:04.712659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation bc88b593-643d-46bc-8ff5-9889c6ab2aa1 · inbound

PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios cites this paper.

PostureObjectstitch: Anomaly Image Generation Considering Assembly Relationships in Industrial Scenarios Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-10T14:20:30.653088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.