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Paper Citation Record · LEDGER

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model

As of 23 August 2026, this Paper Citation Record lists 17 of 17 outbound references and 0 inbound Pith citation observations for arXiv:2508.03925.

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

pith.paper-citation-record.v1
2508.03925 v1

Coverage vector

measured 17 of 17 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T01:06:52.754807Z

measured 17 of 17 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

17 of 17 outbound references displayed

  • verified exact1
  • verified fuzzy15
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4f7ae1ee-d052-467e-a0c4-a0f36c768c6d · outbound

This paper cites Learning Representations and Generative Models for 3D Point Clouds.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Learning Representations and Generative Models for 3D Point Clouds

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:55.568858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.419940Z digest=sha256:a5ebec59e0c11a37bfdfb4c3cf44e21b481aee3d11306168aa42e554bdac71bd

Observation c0d7c8d8-e7a2-43f4-b3a7-b8d02816ccf1 · outbound

This paper cites ShapeWorks: Particle-Based Shape Correspondence and Visualization Software.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model ShapeWorks: Particle-Based Shape Correspondence and Visualization Software

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:55.259395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.494070Z digest=sha256:7e38d07ddcdb43e97c0f446f75f4a6117625015df9291736127f3fe2d24253ca

Observation 9cee5b35-601e-49a6-9ca9-e4dd882d1a77 · outbound

This paper cites Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain Images

Reference 3

Resolution
verified exact
local_arxiv, observed 2026-08-06T01:06:53.008285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.579381Z digest=sha256:16d36e30d7fadf835de25b3dfc8260e1f490892f1a218609529a798972f86c13

Observation b6fdc686-e254-4973-a2c3-d45d98859831 · outbound

This paper cites BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T01:06:51.647843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T01:06:51.647843Z digest=sha256:4c047cfe51452bdae59d1c50f511ca23eb54a1cc2b0499e63076032d0b01c95a

Observation 3211e021-389d-4998-9c8a-b233ac73b61d · outbound

This paper cites Denoising Diffusion Probabilistic Models.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Denoising Diffusion Probabilistic Models

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.989809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.724141Z digest=sha256:7cb85c81ff754277de3ae451698161ea897b1fe963c93a9a0c41e66f8c368852

Observation 49dc715d-0dc1-4730-9d4a-6b7512863d94 · outbound

This paper cites Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in The Data Manifold.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in The Data Manifold

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.795913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.786618Z digest=sha256:cf3c4e670dd2090da6dfc6959223eb5854c75451c3ba71e69bddde636c22adb2

Observation bce8c248-687d-4133-bd17-46b27b597913 · outbound

This paper cites Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.621364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.887582Z digest=sha256:132528200fa5d003ea0b12e5530bf35135d3d599f1363b06be133f866c5d5719

Observation cde36c39-5b2d-495e-af95-d0c2c2c9272a · outbound

This paper cites OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model OASIS-3: Longitudinal Neuroimaging, Clinical, and Cognitive Dataset for Normal Aging and Alzheimer Disease

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.462609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.974389Z digest=sha256:24230523bb245955697b8b3e42397dc00f719e89406d9408231a2359d26338b0

Observation c0d56429-1a75-404c-9c4d-e859c8aeab1b · outbound

This paper cites Diffusion Probabilistic Models for 3D Point Cloud Generation.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Diffusion Probabilistic Models for 3D Point Cloud Generation

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.287791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.064054Z digest=sha256:f1187865d67d1f194c2c2ddb68cc42e5e04db9e7fd13790f78ae32dff4202d73

Observation c53ea988-ff84-44d1-9d75-714f5be37af5 · outbound

This paper cites Reliable Fidelity and Diversity Metrics for Generative Models.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Reliable Fidelity and Diversity Metrics for Generative Models

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:54.109204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.158212Z digest=sha256:1b7bc6f33f11e720febd5df200042b7d9518fc94b3e9cb8f88c345501d251675

Observation 23815d70-9ef4-4196-a7c4-0932ba3bf6d3 · outbound

This paper cites Attention U-Net: Learning Where to Look for the Pancreas.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Attention U-Net: Learning Where to Look for the Pancreas

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.975873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.240370Z digest=sha256:e912b7f36aff5ff16099021a087db1a1a9142f1f0df14af63f9327d9d7acec70

Observation 86447b86-3695-4d29-809b-4e70406d3615 · outbound

This paper cites Brain imaging generation with latent diffusion models.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Brain imaging generation with latent diffusion models

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.813793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.331465Z digest=sha256:8edc89321ab6a0127a7922166cb1c8a5a553c16b013277c9153f4bcd8583fb76

Observation 5575423c-f3b0-4827-9ac5-b3aad70756ab · outbound

This paper cites PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.640915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.454404Z digest=sha256:b508017d27e667ecbd8ee3c8a0cf969c29c9f648162de5a42b03fc050e9f4a8f

Observation 7acdd7f5-47ee-4999-99a2-0580dd6c9396 · outbound

This paper cites Attention is all you need.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Attention is all you need

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.500692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.542513Z digest=sha256:5feaea7d638316aabfbdb7ae2603abd1da73cfdf273a8f598fb607bd52c7c5af

Observation 7f67b27f-454f-4eeb-a3d3-36ed7dec95e2 · outbound

This paper cites Pointflow: 3d point cloud generation with continuous normalizing flows.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Pointflow: 3d point cloud generation with continuous normalizing flows

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.419230Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.616210Z digest=sha256:fee2f9d6a8d74723027688b6c15440fd7abad82594745b3e1d9539856a7bc842

Observation 993c4ab3-3f3f-48cf-957d-a6744799adfd · outbound

This paper cites LION: Latent Point Diffusion Models for 3D Shape Generation.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model LION: Latent Point Diffusion Models for 3D Shape Generation

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.271624Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.683030Z digest=sha256:090091511cbaefda13f6af7b0f6f68fa511df8bed0ead1cb9750726a37224b39

Observation 9ab25b71-11de-4ad5-89ed-daa562e7bf42 · outbound

This paper cites Quantifying Hippocampal Shape Asymmetry in Alzheimer’s Disease Using Optimal Shape Correspondences.

Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model Quantifying Hippocampal Shape Asymmetry in Alzheimer’s Disease Using Optimal Shape Correspondences

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T01:06:53.140341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.754807Z digest=sha256:166a18eecd81dd8e490ccd7735ff1cc76e245e52c64e62a859d4a0f2b0eaae61

Pith citing papers

No inbound Pith citation observations are available.