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Paper Citation Record · LEDGER

PointConv: Deep Convolutional Networks on 3D Point Clouds

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1811.07246.

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

pith.paper-citation-record.v1
1811.07246 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:46:05.345914Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

103
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation fc664ae0-3131-4edd-b582-acf7abc6f15c · inbound

Interpolated Convolutional Networks for 3D Point Cloud Understanding cites this paper.

Interpolated Convolutional Networks for 3D Point Cloud Understanding PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-14T13:46:05.345914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T13:46:05.345914Z digest=sha256:c876a5c0c9e2b96834cd9a336b9a9e850365396a0c8d9b0e142189ad5b1a4050

Observation f366c65c-cc2d-4dae-a74e-9d3abcb8649f · inbound

StarNet: Targeted Computation for Object Detection in Point Clouds cites this paper.

StarNet: Targeted Computation for Object Detection in Point Clouds PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-14T10:32:34.910029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:32:34.910029Z digest=sha256:c9554aa0b9aaf8c631a128042c24721803e28f73d57397772171c34f17ec5254

Observation 38bc305d-6474-414d-a162-6b9f692214e7 · inbound

Improving the Transferability of 3D Point Cloud Attack via Spectral-aware Admix and Optimization Designs cites this paper.

Improving the Transferability of 3D Point Cloud Attack via Spectral-aware Admix and Optimization Designs PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 77

Resolution
unresolved
no resolver link, observed 2026-08-11T14:00:01.633209Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:00:01.633209Z digest=sha256:de22517e0b86f6552229aa6380e665ed39aa39728d236776c0437704de5c88a0

Observation 666672ba-af33-4b69-9d57-b50170379067 · inbound

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation cites this paper.

Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-05T15:27:12.690543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T15:27:12.690543Z digest=sha256:4c45cbfbe2c15dbc88dc83e287f4b2b387b288ad8c3241af784f7776be003949

Observation a81b9cb1-6aa6-445e-bf34-0de55ae360aa · inbound

Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference cites this paper.

Fail2Progress: Learning from Real-World Robot Failures with Stein Variational Inference PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-05T12:19:11.858142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T12:19:11.858142Z digest=sha256:beffb06464d2f9a0666b8f0bc6469214e2dd1135bfead7d1b3ed96f9fc295486

Observation d767d2fb-8d6e-455f-889a-2b86b123091f · inbound

TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis cites this paper.

TRELLIS-Enhanced Surface Features for Comprehensive Intracranial Aneurysm Analysis PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-05T11:11:44.937614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T11:11:44.937614Z digest=sha256:6b7dced41ecb1bcc74088b6f905fa504d3449d9a504e41f51b8fe89e69967194

Observation a9a027c9-25ec-47b9-af43-a8161c10568a · inbound

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments cites this paper.

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-09T06:00:33.776554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-08T19:15:57.936114Z digest=sha256:05545446f41aa7d32241344e58699db27e93ef72b8513ab8bf7378fc37b4eb8b

Observation 6566bdd3-ca6b-478b-88e7-c38976e4a675 · inbound

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments cites this paper.

From Spherical to Gaussian: A Comparative Analysis of Point Cloud Cropping Strategies in Large-Scale 3D Environments PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-22T09:51:21.645771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-22T09:50:31.499822Z digest=sha256:9acdc490c9a3215892baa19b447186b7119d19f2049779f816a60bb0d4570900

Observation 26ad1551-f55b-4c5d-908b-4b748f787e7f · inbound

Learning Representations from 3D Gaussian Splats cites this paper.

Learning Representations from 3D Gaussian Splats PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-06-29T08:43:15.404906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-06-29T08:38:34.337340Z digest=sha256:1a7694d771ba8d461cfa2d4db20ea70235093de8c64e3da20c5d393291b6f762

Observation 88c74f98-1457-4580-aec4-cd9eb316896c · inbound

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems cites this paper.

Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems PointConv: Deep Convolutional Networks on 3D Point Clouds

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-06-27T17:11:05.555740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=arxiv_source observed=2026-06-27T17:07:27.417845Z digest=sha256:bcbe272bf5b8f78eaab85734a34fb4c502ac9e55710c1828c1a1a4ce48022e0e