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Paper Citation Record · LEDGER

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

As of 8 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 2026-08-08T06:32:00.761636+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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.419940Z digest=sha256:da8b718726afb8407eba3f3ac68ab0056e5706182002f9a79111a51511eff7dd

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.494070Z digest=sha256:a2317029e09f40d34c5733e64d4d85832c0874fc0d9d5d7212a0413960142689

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.579381Z digest=sha256:62c5400d565fcc4cdbd22245d2fe2e07ae543b83109a81bcff6b065687670032

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:c0d05be603916135c59f7ecc689cf1af34d6cf998c74e93356ba7b2a74c04f77

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.724141Z digest=sha256:4380c4ffd96b651ed031da9ecc49ab196be419906a7ac021cc36bb950caeb2ce

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.786618Z digest=sha256:e5bc3380d135f03886103af8f9f503e06b61586ab7b6f5254fa2b7b8e43e7e59

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.887582Z digest=sha256:f18fe05cfabd12d970f8b21c47c60361a1d942d5c665122a1dd1cc1ff618d53b

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:51.974389Z digest=sha256:ead1f3f5d5202027b9fcc8e130626cf2d72d173859c7d1bc0616694dc4a14a0b

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.064054Z digest=sha256:cf808f9d16b6867582eab1ecf7966553f8e8197f884fe0aeb48b0d7e349fb610

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.158212Z digest=sha256:c7326b6655e124440ceb4139200284ff9360c56da6acd8cd5d3f53e47881c7a2

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.240370Z digest=sha256:77aecc55328038b22c9b8216ef89bdb5b1382f74a79c2b84fe6a9b6270c8c330

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.331465Z digest=sha256:f67380bb52857eb172d944c9483432577d883a671a446a3c7d56f5b8f384d5a1

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.454404Z digest=sha256:dcf38947cd9236fe43a363bc96b569b3df4d7e0b401218e299fef7e8c14b9bd5

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.542513Z digest=sha256:39cb0a34fd1f0524fd7e0bb73710ee9c2c4d79befd3d03416102f4cf76bfdd0f

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.616210Z digest=sha256:1fb324514d79e501210295c486f1c0c7184226ff75a479424d1d08aedfd41765

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.683030Z digest=sha256:e515ee904650e770bc77b3b490848326af770be5dc9243f5c6e7a59ae1d91b45

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 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-08-06T01:06:52.754807Z digest=sha256:95d1b098a7afb6e43a099b59cfa8f89ec8632e337cd2224200f77b36fd1eeb16

Pith citing papers

No inbound Pith citation observations are available.