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Paper Citation Record · LEDGER

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression

As of 21 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.15686.

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

pith.paper-citation-record.v1
2507.15686 v1

Coverage vector

measured 35 of 35 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T15:30:37.831487Z

measured 35 of 35 standing notices

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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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3be9db4c-70de-4801-b069-776ec82eaad3 · outbound

This paper cites ISO/IEC JTC1/SC29/WG7 MDS20352/N00100, 2021.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression ISO/IEC JTC1/SC29/WG7 MDS20352/N00100, 2021

Reference 1

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Observation 4e36be7a-2756-46b6-82c0-dd1b15a1c082 · outbound

This paper cites ISO/IEC JTC1/SC29/WG7 MDS24176/N00942, 2024.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression ISO/IEC JTC1/SC29/WG7 MDS24176/N00942, 2024

Reference 2

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Observation 5f605396-229c-43ce-a2a3-5d8767f16232 · outbound

This paper cites End-to-end Optimized Image Compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression End-to-end Optimized Image Compression

Reference 3

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Observation e47280f6-2241-4c46-87f8-9fba87ae8650 · outbound

This paper cites Variational image compression with a scale hyperprior.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Variational image compression with a scale hyperprior

Reference 4

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Observation 51c35ebb-4f9b-454b-b7b9-e0c447cd9b1c · outbound

This paper cites Muscle: Multi sweep compression of lidar using deep entropy models.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Muscle: Multi sweep compression of lidar using deep entropy models

Reference 5

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Observation 2a1a832b-7bb1-43d2-a844-2db4bda943cc · outbound

This paper cites 3d point cloud compression: A survey.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression 3d point cloud compression: A survey

Reference 6

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Observation 91d85fd5-d863-4949-8a04-ab7e33640632 · outbound

This paper cites Compression of sparse and dense dynamic point clouds—methods and standards.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Compression of sparse and dense dynamic point clouds—methods and standards

Reference 7

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Observation f1a9a994-1cd1-4af9-95b2-a0fc8f269555 · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neu- ral networks.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression 4d spatio-temporal convnets: Minkowski convolutional neu- ral networks

Reference 8

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Observation a380f1c4-9385-45c7-8cbc-e9241c2e7e0d · outbound

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LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Unresolved cited work

Reference 9

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Observation d16e71c7-d304-47e7-abc9-6fbe7f802140 · outbound

This paper cites Multiscale latent-guided entropy model for lidar point cloud compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Multiscale latent-guided entropy model for lidar point cloud compression

Reference 10

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Observation f5a76c4d-16e1-45a1-8d00-0a7eeaa2f856 · outbound

This paper cites Oc- tattention: Octree-based large-scale contexts model for point cloud compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Oc- tattention: Octree-based large-scale contexts model for point cloud compression

Reference 11

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Observation b0d05f6b-26f5-4395-ae57-e7c4c1113c77 · outbound

This paper cites An overview of ongoing point cloud compression standardiza- tion activities: video-based (v-pcc) and geometry-based (g- pcc).

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression An overview of ongoing point cloud compression standardiza- tion activities: video-based (v-pcc) and geometry-based (g- pcc)

Reference 12

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Observation e81652d5-a15d-4d03-a573-c4eb567aa0e2 · outbound

This paper cites An overview of ongoing point cloud compression standardiza- tion activities: Video-based (v-pcc) and geometry-based (g- pcc).

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression An overview of ongoing point cloud compression standardiza- tion activities: Video-based (v-pcc) and geometry-based (g- pcc)

Reference 13

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Observation e268968a-b3d1-4697-9125-b7ca0ade61bc · outbound

This paper cites Mpeg-pcc-tmc13.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Mpeg-pcc-tmc13

Reference 14

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Observation b4bd7d8b-2b84-4667-9123-e5656cb3b573 · outbound

This paper cites Mpeg-pcc-tmc2.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Mpeg-pcc-tmc2

Reference 15

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Observation 5413c25e-31af-46d3-8be5-dbab9353eea4 · outbound

This paper cites Learning neural volumetric field for point cloud geometry compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Learning neural volumetric field for point cloud geometry compression

Reference 16

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Observation 6d0bddda-5fc2-4aa5-90c5-13c60c2c2167 · outbound

This paper cites Octsqueeze: Octree-structured en- tropy model for lidar compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Octsqueeze: Octree-structured en- tropy model for lidar compression

Reference 17

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Observation 5e944a63-0c84-4a07-8421-3a41202792d0 · outbound

This paper cites Lvac: Learned volumetric at- tribute compression for point clouds using coordinate based networks.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Lvac: Learned volumetric at- tribute compression for point clouds using coordinate based networks

Reference 18

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Observation 394b7fe3-f57b-46e7-a6a4-eda21aaf503f · outbound

This paper cites Jang, Marius Preda, Khaled Mammou, Alexis M.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Jang, Marius Preda, Khaled Mammou, Alexis M

Reference 19

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Observation f6ca1882-c229-4cc9-98fc-bb40bcb484f0 · outbound

This paper cites Owlii dy- namic human mesh sequence dataset.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Owlii dy- namic human mesh sequence dataset

Reference 20

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Observation cde22cc1-83b4-43e5-9073-6fab3c11348a · outbound

This paper cites Adam: A method for stochastic optimization.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Adam: A method for stochastic optimization

Reference 21

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Observation 80e9fb28-30ac-41b6-aba9-28862bb050d2 · outbound

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LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Unresolved cited work

Reference 22

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Observation ad5181fd-145d-43d6-b91a-39a6df3ea1b7 · outbound

This paper cites Multiscale deep context modeling for lossless point cloud geometry compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Multiscale deep context modeling for lossless point cloud geometry compression

Reference 23

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Observation d211fda1-071f-480b-8ba0-20837dea20ce · outbound

This paper cites Sparsepcgc.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Sparsepcgc

Reference 24

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Observation a7aeda87-e47f-41f3-9770-8774a9487cca · outbound

This paper cites Pytorch: An im- perative style, high-performance deep learning library.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Pytorch: An im- perative style, high-performance deep learning library

Reference 25

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Observation af444108-367c-4872-8f8b-8ffe91b8e93a · outbound

This paper cites Signal compression via neural implicit represen- tations.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Signal compression via neural implicit represen- tations

Reference 26

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Observation 88069add-9c2a-4129-bce1-ecf36595b833 · outbound

This paper cites Survey on deep learning-based point cloud compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Survey on deep learning-based point cloud compression

Reference 27

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Observation fd2bef58-2eb4-4722-a4d3-17b540869188 · outbound

This paper cites Point cloud compression with implicit neural representations: A unified framework.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Point cloud compression with implicit neural representations: A unified framework

Reference 28

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Observation 5ce270e8-ed16-4557-a457-6c7d6836071a · outbound

This paper cites Emerging mpeg standards for point cloud compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Emerging mpeg standards for point cloud compression

Reference 29

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Observation f14588a4-ddd7-4ced-bfde-2f2ed13f9c5a · outbound

This paper cites Efficient hier- archical entropy model for learned point cloud compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Efficient hier- archical entropy model for learned point cloud compression

Reference 30

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Source-reported events for the cited work

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Observation 37a64467-d86d-4e4f-baa7-f719c5fce0da · outbound

This paper cites Multi- scale point cloud geometry compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Multi- scale point cloud geometry compression

Reference 31

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Observation 134cb298-e576-4aa6-8b8b-c537eceff3ad · outbound

This paper cites Sparse tensor-based multi- scale representation for point cloud geometry compression.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Sparse tensor-based multi- scale representation for point cloud geometry compression

Reference 32

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Source-reported events for the cited work

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Observation 38c2b574-2486-47c6-a7e2-28cbb007c0ab · outbound

This paper cites A versatile point cloud compressor using universal multiscale conditional coding – part i: Geometry.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression A versatile point cloud compressor using universal multiscale conditional coding – part i: Geometry

Reference 33

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Source-reported events for the cited work

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Observation b5a3d0bf-8177-4ece-9824-b369cf1fdee3 · outbound

This paper cites Lightweight super resolution network for point cloud geometry compres- sion.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Lightweight super resolution network for point cloud geometry compres- sion

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:30:38.270473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:30:37.745689Z digest=sha256:f075dc7eea9090af18239e9536a6f7544bfb6a061d4af49e2e5248cee2a64de1

Observation 0b7f817a-1ad6-4885-b134-a268ba7e07ac · outbound

This paper cites Detail of parameters The details of the parameters in our experiment are listed in Tab.

LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression Detail of parameters The details of the parameters in our experiment are listed in Tab

Reference 35

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T15:30:38.059834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T15:30:37.831487Z digest=sha256:9dfedc5d32bb895930223e71a5350b7311b247e55c490a03d547744ad246d16f

Pith citing papers

No inbound Pith citation observations are available.