Paper Citation Record · LEDGER

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation

As of 12 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2412.03052.

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

pith.paper-citation-record.v1
2412.03052 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T22:53:30.109016Z

measured 29 of 29 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

29 of 29 outbound references displayed

verified exact0
verified fuzzy22
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 63d151fa-71e5-4192-9aee-1d9effb74dcc · outbound

This paper cites A concise and provably informative multi-scale signature based on heat diffusion.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation A concise and provably informative multi-scale signature based on heat diffusion

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.787824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 527703de-ea3e-4724-b85c-6250765ba0c6 · outbound

This paper cites Aligning point cloud views using persistent feature histograms.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Aligning point cloud views using persistent feature histograms

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.674284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 550b1c30-d911-459b-82c8-ff3d54c187a8 · outbound

This paper cites Using spin images for efficient object recognition in cluttered 3d scenes.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Using spin images for efficient object recognition in cluttered 3d scenes

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.597807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 524c9e1b-42ad-46de-b8d2-091562ac4ae8 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation 3d shapenets: A deep representation for volumetric shapes

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.581020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bb1dd74c-5715-4a92-8765-825dde0385d0 · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.549521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 60b09870-e8af-42ce-a423-e22a2d8523bf · outbound

This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:29.408304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5245095-4494-41f1-b00c-dfaee9d1fc99 · outbound

This paper cites Dynamicedge-conditionedfilters in convolutional neural networks on graphs.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Dynamicedge-conditionedfilters in convolutional neural networks on graphs

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.463945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb0b995e-f40e-43a8-b180-f6650258b58d · outbound

This paper cites Grid-gcn for fast and scalable point cloud learning.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Grid-gcn for fast and scalable point cloud learning

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.348001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fc015cdb-cdc2-4569-a99c-9b2fd3d6198e · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Dynamic graph cnn for learning on point clouds

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.329998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1f2d7eec-14be-4124-adbd-978219746970 · outbound

This paper cites Voxnet: A 3d convolutional neu- ral network for real-time object recognition.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Voxnet: A 3d convolutional neu- ral network for real-time object recognition

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.313801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 051f3d7e-5698-4afc-adc6-08bbd6bd1776 · outbound

This paper cites Escape from cells: Deep kd-networks for the recognition of 3d point cloud models.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Escape from cells: Deep kd-networks for the recognition of 3d point cloud models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.289259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3c33d808-232f-4d9f-9c5b-718c638ac4f4 · outbound

This paper cites Octnet: Learning deep 3d representations at high resolutions.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Octnet: Learning deep 3d representations at high resolutions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.223091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation dd5da914-1609-43eb-b374-741ed5240e49 · outbound

This paper cites DenseNet: Implementing Efficient ConvNet Descriptor Pyramids.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation DenseNet: Implementing Efficient ConvNet Descriptor Pyramids

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:29.711207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a89c68f5-6aa9-49df-9a84-c646602f8617 · outbound

This paper cites Interpolated convolu- tional networks for 3d point cloud understanding.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Interpolated convolu- tional networks for 3d point cloud understanding

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:31.091410Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a2652c30-0db6-4a60-bb3b-3dde2c59752e · outbound

This paper cites Modelinglocalgeometric structure of 3d point clouds using geo-cnn.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Modelinglocalgeometric structure of 3d point clouds using geo-cnn

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.959077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 847dcff5-a6fd-4613-b17c-65005abdc28e · outbound

This paper cites An introduction to kernel and nearest-neighbor nonpara- metric regression.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation An introduction to kernel and nearest-neighbor nonpara- metric regression

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.941359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3ddfd262-1069-462f-b25b-8cbcee1e14aa · outbound

This paper cites an unresolved cited work.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:53:30.923790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3f5dc53b-68d6-44a2-8076-f33419f96aee · outbound

This paper cites an unresolved cited work.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:53:30.907468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 41d759e5-25ae-460e-ad94-d8f3391ea5a3 · outbound

This paper cites A-cnn: Annularly convo- lutional neural networks on point clouds.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation A-cnn: Annularly convo- lutional neural networks on point clouds

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.872084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9bce82a8-e57b-454a-afd0-1541045c21f5 · outbound

This paper cites PCT: Point cloud transformer.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation PCT: Point cloud transformer

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:29.866940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7665f04b-0632-44ea-bd8d-c5946b79b976 · outbound

This paper cites Escape from cells: Deep kd-networks for the recognition of 3d point cloud models.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Escape from cells: Deep kd-networks for the recognition of 3d point cloud models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.788942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 09f1a4f3-0e2b-44b0-9a32-7be34a3c0917 · outbound

This paper cites Point Convolutional Neural Networks by Extension Operators.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Point Convolutional Neural Networks by Extension Operators

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T22:53:29.897346Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df336821-11f6-473e-98f2-b213855933b2 · outbound

This paper cites Di, and B.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Di, and B

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.768204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fb87f4d0-9774-4b16-a058-1fef653fd05e · outbound

This paper cites an unresolved cited work.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-11T22:53:30.728331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b82a8ac-fa03-4ac5-8b7a-9414dc2abeaf · outbound

This paper cites Relation- shape convolutional neural network for point cloud analysis.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Relation- shape convolutional neural network for point cloud analysis

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.658030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1344b90c-2f41-49ac-b607-0258b66d9fc7 · outbound

This paper cites Kpconv: Flexible and deformable convolution for point clouds.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Kpconv: Flexible and deformable convolution for point clouds

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.505361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation cc439f9f-0cd2-4077-b081-69324ec93456 · outbound

This paper cites Pointgrid: A deep network for 3d shape under- standing.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Pointgrid: A deep network for 3d shape under- standing

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.382270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bcb9c640-4cc7-4e9e-bfea-8c8dbb0bbac5 · outbound

This paper cites A scal- able active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation A scal- able active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.360482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a0e5308a-bfbf-4290-9125-265d18c53fab · outbound

This paper cites Pointcnn: Convolution on x-transformed points.Advances in neural information processing systems, 31, 2018.

Point-GR: Graph Residual Point Cloud Network for 3D Object Classification and Segmentation Pointcnn: Convolution on x-transformed points.Advances in neural information processing systems, 31, 2018

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T22:53:30.302149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.