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Paper Citation Record · LEDGER

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2412.12617.

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

pith.paper-citation-record.v1
2412.12617 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:58:22.098829Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:22:02.528400Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T05:47:37.375945Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact0
  • verified fuzzy41
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8039e9ab-3f06-47d4-8918-1150827427e1 · outbound

This paper cites write newline.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-11T13:58:21.784345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:58:21.784345Z digest=sha256:0725379ffa576963261f17e99a8353b76ff491b10acd1085b6bd29da01ea5d85

Observation d2a2a76d-4e34-4165-8903-a1e338a35863 · outbound

This paper cites P NI: Industrial Anomaly Detection using Position and Neighborhood Information.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection P NI: Industrial Anomaly Detection using Position and Neighborhood Information

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.880659Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.792950Z digest=sha256:f087027fe3fa8d7ead77d3699ec31d2b22681a639d24fd560fd7036f0f473bea

Observation 85b39f37-9f9f-4de1-8e2d-c16be836a93e · outbound

This paper cites A nomaly Detection in 3D Point Clouds using Deep Geometric Descriptors.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection A nomaly Detection in 3D Point Clouds using Deep Geometric Descriptors

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.869295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.801414Z digest=sha256:b1e068b788afe2b6d2ef7ec8a797e0fc6e8031965443d05ba0b49e030614e8ca

Observation 908b7c93-0086-4f1e-bff1-326ec4f07e24 · outbound

This paper cites A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection A RANSAC-Based Approach to Model Fitting and Its Application to Finding Cylinders in Range Data

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.809787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.810460Z digest=sha256:37d038e52f6946a2d7c1b19c6edde58b9191cbdee9c1ed0fc1dedc9fdead57b2

Observation b73d9a41-a294-4a7b-b351-622762ee6c69 · outbound

This paper cites C omplementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection C omplementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection

Reference 5

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verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.687002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.821075Z digest=sha256:de641120ea9a72d35767ea6f13d45b50262ab6f88e71f5aca6f4b2ee293e06a8

Observation 4d88aa21-8c5c-47e3-a16a-8d61faf16553 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection ShapeNet: An Information-Rich 3D Model Repository

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-11T13:58:21.846753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:58:21.846753Z digest=sha256:1bde5603887c732892216debf35f0891cff3e08d737e5e734df2075dc80d7f57

Observation f581d783-ebe0-45c1-8c41-ea044456f99b · outbound

This paper cites 4 D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection 4 D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.675582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.856060Z digest=sha256:5e67bca5cd8d8805f47b31e919e6cbdce659bab3cfe2a531f441cc240403745c

Observation e4c491ed-9577-42cc-8320-80adf38a5077 · outbound

This paper cites F ully Convolutional Geometric Features.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection F ully Convolutional Geometric Features

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.597022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.860146Z digest=sha256:808289864af2c918e0885c038e1a07a4297c17b41a34c440548f39f92e9a7683

Observation 9facaa83-f4c8-46ee-b782-ab218263dd44 · outbound

This paper cites S ceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S ceneFun3D: Fine-Grained Functionality and Affordance Understanding in 3D Scenes

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.485785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.863698Z digest=sha256:4adb0c0f7ebb621988a1c44c78fedc49b3ac051d48e427fc80fd88421e2c0f21

Observation 82e25df5-d170-450d-a41b-546c6779cde5 · outbound

This paper cites S parse 3D Convolutional Neural Networks.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S parse 3D Convolutional Neural Networks

Reference 10

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verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.474252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.867272Z digest=sha256:7c595923a1cf13d3473f84f20ede7bb0da8d4e248ffc2ddef0b432527bc3640e

Observation 4506c93e-3f4a-40f5-8469-254ecb6f840d · outbound

This paper cites C FLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection C FLOW-AD: Real-Time Unsupervised Anomaly Detection With Localization via Conditional Normalizing Flows

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.462791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.870958Z digest=sha256:f79eea6fd59d6c28a66b3fc7b0cafa302cb4848ed5431cff6a39796efed414a2

Observation e72f8435-d38a-4dc5-9c7d-9df4982bc26e · outbound

This paper cites G enerative Sparse Detection Networks for 3D Single-shot Object Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection G enerative Sparse Detection Networks for 3D Single-shot Object Detection

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.452375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.874413Z digest=sha256:a569bb8bbf461bfb079b80441dcc7d132685aaf8fe09fb7bfc3d0f4fa298f454

Observation 08dde659-c68c-423e-842f-1b53a7b94c07 · outbound

This paper cites D yCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection D yCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.441731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.878306Z digest=sha256:024a46240e72be57fad706c61bf0833f6b83e1a6797a9febb3acd9f77bb1d85e

Observation 0f1bfc59-b29d-4f07-80d2-ebb72140ee51 · outbound

This paper cites B ack to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection B ack to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.313608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.882320Z digest=sha256:c6b9f16d19bf9214dfc8f46274ad666d03206560ddb27dc7c460c434b00f48c4

Observation 820b241e-d491-419e-a141-5666c4a47588 · outbound

This paper cites A nomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection A nomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.166390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.885813Z digest=sha256:36d0c260cdbf5b3aa60de597c2befa4c5af50b0b726449ea3fb2e35ad14cc014

Observation d6e2f846-92c6-420d-a8fd-1c7bae8712fd · outbound

This paper cites B idirectional Projection Network for Cross Dimension Scene Understanding.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection B idirectional Projection Network for Cross Dimension Scene Understanding

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.156092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.889024Z digest=sha256:ba7d3980d74001ac6ee9a78757f71af9bf2d1860fde4cca87f8176e821190d50

Observation 0d4bcead-d984-46aa-934a-042c316c05cb · outbound

This paper cites S elf-Supervised Masking for Unsupervised Anomaly Detection and Localization.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S elf-Supervised Masking for Unsupervised Anomaly Detection and Localization

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.146028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.892344Z digest=sha256:de183ffcac7ce4e4599518de7ece0b11302b767b5d577ae2ff25d87512993bd5

Observation 8d2d6cae-1480-455d-b929-0b6dc345cbb0 · outbound

This paper cites P ointGroup: Dual-Set Point Grouping for 3D Instance Segmentation.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection P ointGroup: Dual-Set Point Grouping for 3D Instance Segmentation

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.134219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.895656Z digest=sha256:d53ff9c6a3428af9053429b561a9ee934b90493216940dd1a045cd4580319f18

Observation 9099f09a-a752-4ab3-8c72-fa532fc91422 · outbound

This paper cites F APM: Fast Adaptive Patch Memory for Real-Time Industrial Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection F APM: Fast Adaptive Patch Memory for Real-Time Industrial Anomaly Detection

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.122421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.899255Z digest=sha256:85f0afaa261671e04a4ff7ab336a85500b97a35fb0d8cc5f6efdf1f93becbeca

Observation 3672bc76-2e25-4ec5-b1a1-3f197df6fa19 · outbound

This paper cites C utPaste: Self-Supervised Learning for Anomaly Detection and Localization.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection C utPaste: Self-Supervised Learning for Anomaly Detection and Localization

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.110911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.902751Z digest=sha256:359c44d6c8efb2b9c0cf11ad16ee0ccc4cc0a7d15563bcbb9b6fb8d14b1e2355

Observation b5c5cf13-c529-4f7b-871d-ee511631c2eb · outbound

This paper cites T owards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection T owards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Network

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.099944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.906520Z digest=sha256:564be1384e05b4679dc23d78f3a8355c8585ff18dda4b204a69c938d0ef23c88

Observation 5174bcc1-2816-4e09-a99c-c1d483c4715f · outbound

This paper cites R eal3D-AD: A Dataset of Point Cloud Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection R eal3D-AD: A Dataset of Point Cloud Anomaly Detection

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.074584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.909934Z digest=sha256:84477e8994b4dd5a379fff827ab27b261a334d3bd2f75627770be42ac6a67c17

Observation 82823966-26be-4fd0-b380-78846b9c52f0 · outbound

This paper cites S impleNet: A Simple Network for Image Anomaly Detection and Localization.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S impleNet: A Simple Network for Image Anomaly Detection and Localization

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:23.033999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.913657Z digest=sha256:92766cb4c1e65ba92c4ff284a4eb39dda0229b0e9ded4ac8abe0c567d7d37ebb

Observation 9130d10f-6b7b-4776-a03c-84083551b2cd · outbound

This paper cites S GDR: Stochastic Gradient Descent with Warm Restarts.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S GDR: Stochastic Gradient Descent with Warm Restarts

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.814227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.918293Z digest=sha256:84fb0b4a156dbab0ced98d2c5beec85774682cb30c0f7b6a7db6f0771d76d300

Observation 7d9008e4-030b-414c-ad02-ebd92cbffd15 · outbound

This paper cites M asked Autoencoders for Point Cloud Self-Supervised Learning.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection M asked Autoencoders for Point Cloud Self-Supervised Learning

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.732111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.921922Z digest=sha256:c3742371649861fc412688a3498c0c757696a8a720286ec2557bdf70afea0c7b

Observation 51729caf-4055-4561-8be7-9eedfd32a231 · outbound

This paper cites I npainting Transformer for Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection I npainting Transformer for Anomaly Detection

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.702927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.925686Z digest=sha256:cd0500e91249273c022008b628bda30a7d3c32c2c054de026644f7d8f077fa59

Observation 8889b3cc-4134-4f07-9faa-199b2b6b137e · outbound

This paper cites T owards Total Recall in Industrial Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection T owards Total Recall in Industrial Anomaly Detection

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.692688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.937382Z digest=sha256:88f53a33924c4fbbdcab18cd64fd5cd309719343ea6c44ffbb3d8bc051d9ac8d

Observation a394505c-7ce4-45fd-aef9-a95af58a6901 · outbound

This paper cites S ame Same but DifferNet: Semi-Supervised Defect Detection With Normalizing Flows.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection S ame Same but DifferNet: Semi-Supervised Defect Detection With Normalizing Flows

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.681816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.940877Z digest=sha256:cfd94520a44f25b3d2a1a4dcbb822e833e2c2bcb5203b0fe3bfd17c516f70dd2

Observation 6da21831-2257-451b-bdda-e502fbc6446f · outbound

This paper cites A symmetric Student-Teacher Networks for Industrial Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection A symmetric Student-Teacher Networks for Industrial Anomaly Detection

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.671313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.945043Z digest=sha256:9eb591b14fc23cd628cd96feb3f801a30d3d240379188e458cfa27ba201b4b97

Observation 0e58ee58-548c-4cd2-9599-1775370c90ec · outbound

This paper cites F ast Point Feature Histograms (FPFH) for 3D Registration.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection F ast Point Feature Histograms (FPFH) for 3D Registration

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.658456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.949216Z digest=sha256:1c0e919345cfaaae4d3cfc42c74a25dd688a6139dcbff50ad36c207b67b4cbd4

Observation ab9d4a67-df47-4572-92d5-ab19d2bb71cc · outbound

This paper cites N atural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection N atural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.647552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.953252Z digest=sha256:41e683d9549c80b73f202ab5885cfd444a166a9d4497ae9707479188b284643a

Observation 7a7de901-863f-4e3a-8519-a7429f654595 · outbound

This paper cites M ask3D: Mask Transformer for 3D Semantic Instance Segmentation.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection M ask3D: Mask Transformer for 3D Semantic Instance Segmentation

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.627095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.956950Z digest=sha256:6d0a75350c08964581f476f5c27e97b31fd38dc3d0c48e1207eb8bd68da15746

Observation 429cdf9b-bc11-4221-bd22-5a67cfcf2b62 · outbound

This paper cites Soft Group for 3D Instance Segmentation on Point Clouds.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection Soft Group for 3D Instance Segmentation on Point Clouds

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.595978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.960715Z digest=sha256:15409205f92da90c690b619cdffb4b1005a66136160864cdb2911ad3fe916e5d

Observation 44387fc1-0ed3-44bc-b6a7-9430f0019f89 · outbound

This paper cites M ultimodal Industrial Anomaly Detection via Hybrid Fusion.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection M ultimodal Industrial Anomaly Detection via Hybrid Fusion

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.444964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:21.981003Z digest=sha256:8d317dcc79da8845baccfc04463877d660bb1b08227b9aa21e4df08270d85045

Observation 976c0048-0ea2-40ef-8b0d-e2fe0b0f8863 · outbound

This paper cites P ushing the Limits of Fewshot Anomaly Detection in Industry Vision: GraphCore.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection P ushing the Limits of Fewshot Anomaly Detection in Industry Vision: GraphCore

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.275311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.001603Z digest=sha256:53e45a9de7004b0e1631ec315775a7c04145c5cafe6f8039d28fdef5fcdec6f5

Observation f6b775c0-f1cd-4e28-8f26-7093c592f267 · outbound

This paper cites L earning Semantic Context from Normal Samples for Unsupervised Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection L earning Semantic Context from Normal Samples for Unsupervised Anomaly Detection

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.228565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.040641Z digest=sha256:de128c25db8b3451e573c6ffbb3a96bfc329fc009055393d8d69b4dd8c0d1188

Observation 10701dce-a45b-43a0-8852-045781f7b52b · outbound

This paper cites D RAEM-A Discriminatively Rrained Reconstruction Embedding for Surface Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection D RAEM-A Discriminatively Rrained Reconstruction Embedding for Surface Anomaly Detection

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.217787Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.061714Z digest=sha256:3ecbd63705b0e56544d5e739476b211f4422f9d4fb2f0a10c7f43ed5b0e0b3d9

Observation 2971132d-e154-45b7-9a9c-2cd4ef32d902 · outbound

This paper cites R econstruction by Inpainting for Visual Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection R econstruction by Inpainting for Visual Anomaly Detection

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.205826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.078826Z digest=sha256:dbc2b8a9cc26b868981840805ff3918d2a6a178e6df6228235ef8c55bca46d0f

Observation 2ae9f5e6-5c4e-4e2c-9986-a0901d7fa94a · outbound

This paper cites R ealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection R ealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.193917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.082632Z digest=sha256:12514e3b5890b3eef60e0d72922a85cab195836a3555c6e8d4f52a3715fa1a0e

Observation 391dfa75-bc4b-4936-b5e4-22e91157b7b7 · outbound

This paper cites D ivide and Conquer: 3D Point Cloud Instance Segmentation with Point-Wise Binarization.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection D ivide and Conquer: 3D Point Cloud Instance Segmentation with Point-Wise Binarization

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.183126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.086217Z digest=sha256:50be31e2abe4fa12991b9f17fcb078025b71d197278b27425fddbd65c384b9f4

Observation c52a1ea5-087f-4ff2-94e9-d980b6cf0f3b · outbound

This paper cites R 3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection R 3D-AD: Reconstruction via Diffusion for 3D Anomaly Detection

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.170793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.090307Z digest=sha256:c8dc94b3e7482d795f0e6caaebfa4b1df58922cca39e190b1438c78690a07cef

Observation f0637498-e280-43d4-8035-a1f0b4257e36 · outbound

This paper cites T owards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection T owards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.158145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.094494Z digest=sha256:de745270dbc403a7c6ca36661be912fff4b16dab11507c278e404e92f710d308

Observation ae005143-3cdb-47cd-8fbc-cb73c483a307 · outbound

This paper cites Point Transformer V3: Simpler Faster Stronger.

PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection Point Transformer V3: Simpler Faster Stronger

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T13:58:22.144627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-11T13:58:22.098829Z digest=sha256:831e12e898653da74405d218fe33c79999e744e3acfda62c45c8f8f556b9cc2a

Pith citing papers

Observation 07297bb2-508d-4a05-8243-868e271506bd · inbound

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

A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection

Reference 294

Resolution
unresolved
no resolver link, observed 2026-08-06T17:22:02.528400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:22:02.528400Z digest=sha256:d82efd1772a12366ee64be1676ed4cb06e2d4e5dfee5fbed15b261cfb62e1da2

Observation 5d9de4d7-81e9-4d91-88f6-df2bff345b4d · inbound

C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor cites this paper.

C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisor PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detection

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-06T05:47:37.381584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-08-06T05:47:37.344973Z digest=sha256:d34ff7b61a1c474d87299fc829c6c725f5fb3356c8cf91e36b9bb798ddfbecea