Paper Citation Record · LEDGER

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction

As of 7 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2607.05568.

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

pith.paper-citation-record.v1
2607.05568 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-11T05:41:50.671046Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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verified fuzzy0
unresolved44
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malformed identifier0
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External citation measurements

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Outbound references

Observation 4560de9e-fcb0-40a8-aba9-820921d98e8e · outbound

This paper cites GPT-4 Technical Report.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction GPT-4 Technical Report

Reference 1

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Observation 41b24766-a9a7-4130-8180-fb06a59e7017 · outbound

This paper cites : Iterative superquadric recomposition of 3D objects.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Iterative superquadric recomposition of 3D objects

Reference 2

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Observation 56c88fe8-c2ca-416b-b426-894667ea6ea5 · outbound

This paper cites https://artificialanalysis.ai/image/leaderboard/editing, 2026.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction https://artificialanalysis.ai/image/leaderboard/editing, 2026

Reference 3

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Observation 31eab53a-d595-46c5-8ef1-0a42e6a196fb · outbound

This paper cites an unresolved cited work.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Unresolved cited work

Reference 4

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Observation 59f50118-2ae3-432b-8a61-0a8d2c0fc52d · outbound

This paper cites : Efficiently approximating the minimum-volume bounding box of a point set in three dimensions.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Efficiently approximating the minimum-volume bounding box of a point set in three dimensions

Reference 5

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Observation 32cf53f2-1161-4606-bd24-6ba142f3d795 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction ShapeNet: An Information-Rich 3D Model Repository

Reference 6

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Observation 811df8a1-9b93-4e4b-a96d-200d6f1f3128 · outbound

This paper cites : SuperDec : 3D scene decomposition with superquadric primitives.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : SuperDec : 3D scene decomposition with superquadric primitives

Reference 7

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Observation d317a658-8102-4a0e-8237-93a5f0295b1c · outbound

This paper cites : Image generators are generalist vision learners.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Image generators are generalist vision learners

Reference 8

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Observation 60218f13-bc2f-408e-a937-c412973f85f7 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Gemini: A Family of Highly Capable Multimodal Models

Reference 9

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Observation 8a8d8ba8-e749-4b8f-84d2-def8325fa79e · outbound

This paper cites : Residual primitive fitting of 3D shapes with SuperFrusta.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Residual primitive fitting of 3D shapes with SuperFrusta

Reference 10

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Observation 0cddc0e4-085e-47d8-825e-db263bc1279d · outbound

This paper cites https://blog.google/innovation-and-ai/technology/ai/nano-banana-2/, 2025.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction https://blog.google/innovation-and-ai/technology/ai/nano-banana-2/, 2025

Reference 11

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Observation ba07174e-1b3e-4324-84f1-dd32934b95c2 · outbound

This paper cites : 3D part segmentation via geometric aggregation of 2D visual features.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : 3D part segmentation via geometric aggregation of 2D visual features

Reference 12

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Observation 6ea2f135-e994-4c3b-960b-d2f8a4c55d64 · outbound

This paper cites : Self-supervised learning of hybrid part-aware 3D representations of 2D Gaussians and superquadrics.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Self-supervised learning of hybrid part-aware 3D representations of 2D Gaussians and superquadrics

Reference 13

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Observation 4e73b423-367e-4073-921e-c807c0ad2217 · outbound

This paper cites : Segmentation and Recovery of Superquadrics.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Segmentation and Recovery of Superquadrics

Reference 14

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Observation 33a5d2f8-0b38-4798-91b8-fb5e87970403 · outbound

This paper cites : Learning fine-to-coarse cuboid shape abstraction.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Learning fine-to-coarse cuboid shape abstraction

Reference 15

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Observation 308d835d-b352-473e-9b5e-ed8e2112cd7b · outbound

This paper cites C., Lo W.-Y., Doll \'a r P., Girshick R.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction C., Lo W.-Y., Doll \'a r P., Girshick R

Reference 16

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Observation 1f8466ae-b234-4755-ac72-8e170f1643eb · outbound

This paper cites PASTA: Controllable Part-Aware Shape Generation with Autoregressive Transformers.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction PASTA: Controllable Part-Aware Shape Generation with Autoregressive Transformers

Reference 17

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Observation 8cc779cf-c667-44c8-a4da-f1215a375035 · outbound

This paper cites : P3-SAM : Native 3D part segmentation.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : P3-SAM : Native 3D part segmentation

Reference 18

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Observation 936d3cb9-030b-45bf-8cec-0ef2ebec473c · outbound

This paper cites : PartField : Learning part field representations for generalizable 3D part segmentation.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : PartField : Learning part field representations for generalizable 3D part segmentation

Reference 19

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Observation eca2cd31-a196-4aea-bf3c-26460e610deb · outbound

This paper cites SegviGen: Repurposing 3D Generative Model for Part Segmentation.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction SegviGen: Repurposing 3D Generative Model for Part Segmentation

Reference 20

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Observation 734be800-e045-4219-a6c9-2b9f15e797b9 · outbound

This paper cites : Superquadrics for segmenting and modeling range data.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Superquadrics for segmenting and modeling range data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation 5baadd93-9a46-46b3-b272-ca433b4794c3 · outbound

This paper cites C., Nocedal J.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction C., Nocedal J

Reference 22

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Observation a5460f7e-697f-4212-beea-61934a225f2d · outbound

This paper cites an unresolved cited work.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Unresolved cited work

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation f2ec2d59-bd0c-438a-ab3f-f055a17dc05e · outbound

This paper cites an unresolved cited work.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Unresolved cited work

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation 471a3d0a-8146-4978-9663-c4cee723d56b · outbound

This paper cites : PartSLIP : Low-shot part segmentation for 3D point clouds via pretrained image-language models.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : PartSLIP : Low-shot part segmentation for 3D point clouds via pretrained image-language models

Reference 25

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Observation a06fae78-2a19-418c-9d19-5de24695c56e · outbound

This paper cites A., Aubry M.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction A., Aubry M

Reference 26

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Observation 81988054-8080-4fbb-8648-50534929ca74 · outbound

This paper cites X., Yi L., Tripathi S., Guibas L.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction X., Yi L., Tripathi S., Guibas L

Reference 27

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Observation 6181f85a-e0be-44cf-8f29-59c7b784e79e · outbound

This paper cites an unresolved cited work.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Unresolved cited work

Reference 28

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Observation 657dfb5c-e4d7-42e3-8339-186254b31c26 · outbound

This paper cites O., Geiger A.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction O., Geiger A

Reference 29

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Observation 5f916d91-02c5-41b0-9c9d-d3727ba9d558 · outbound

This paper cites : Neural parts: Learning expressive deformable geometry with invertible neural networks.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Neural parts: Learning expressive deformable geometry with invertible neural networks

Reference 30

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Observation bb6ea5cd-ab8a-41ea-a691-59a47c9f9d10 · outbound

This paper cites Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding

Reference 31

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Observation 7ff52667-f688-4bd4-980a-e9410c92f752 · outbound

This paper cites : Recovery of parametric models from range images: The case for superquadrics with global deformations.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Recovery of parametric models from range images: The case for superquadrics with global deformations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation d0933075-ee7a-44eb-814f-6b263db7dd20 · outbound

This paper cites an unresolved cited work.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction Unresolved cited work

Reference 33

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Observation c2252cc7-b5e4-4de6-b1ba-9ed547498040 · outbound

This paper cites : Learning adaptive hierarchical cuboid abstractions of 3D shape collections.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Learning adaptive hierarchical cuboid abstractions of 3D shape collections

Reference 34

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Observation b02894d5-9ad8-4a31-aefa-50250754b5d6 · outbound

This paper cites : Llm-primitives: Large language model for 3D reconstruction with primitives.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Llm-primitives: Large language model for 3D reconstruction with primitives

Reference 35

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Observation bb9fb91e-eefd-4b42-9e36-1041ee27f6ed · outbound

This paper cites J., Efros A.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction J., Efros A

Reference 36

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Observation 59f0edff-77eb-483b-a6e1-53b3d8d1f47c · outbound

This paper cites : Light-SQ : Structure-aware shape abstraction with superquadrics for generated meshes.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Light-SQ : Structure-aware shape abstraction with superquadrics for generated meshes

Reference 37

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Observation 7b878315-6b61-4328-9134-9d265efb19d4 · outbound

This paper cites MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction MeshSegmenter: Zero-Shot Mesh Semantic Segmentation via Texture Synthesis

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-11T05:47:53.489936Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a6c393c3-ad64-4698-a98f-0b90f4966bdb · outbound

This paper cites : Unsupervised learning of fine structure generation for 3D point clouds by 2D projection matching.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Unsupervised learning of fine structure generation for 3D point clouds by 2D projection matching

Reference 39

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Observation 65a6ed6f-8c0b-4ae1-bf60-699e6034b292 · outbound

This paper cites SAMPart3D: Segment Any Part in 3D Objects.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction SAMPart3D: Segment Any Part in 3D Objects

Reference 40

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Observation b882f9e9-12e4-4f25-be2f-5254ba94ea90 · outbound

This paper cites : PrimitiveAnything : Human-crafted 3D primitive assembly generation with auto-regressive transformer.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : PrimitiveAnything : Human-crafted 3D primitive assembly generation with auto-regressive transformer

Reference 41

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Observation 90cc0ec9-c5c4-4346-88ec-a7ae85cc72e0 · outbound

This paper cites A., Han J., Thomas R., Zhang H., Du Y., Chen H., Engelmann F., You S., Guibas L.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction A., Han J., Thomas R., Zhang H., Du Y., Chen H., Engelmann F., You S., Guibas L

Reference 42

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation 284688fb-f89f-437c-a894-0959710f8603 · outbound

This paper cites : SweepNet : Unsupervised shape abstraction via neural sweeping.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : SweepNet : Unsupervised shape abstraction via neural sweeping

Reference 43

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation a66cf0a3-b859-41e0-b010-f2d777cd0c22 · outbound

This paper cites : Point- SAM : Promptable 3D segmentation model for point clouds.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : Point- SAM : Promptable 3D segmentation model for point clouds

Reference 44

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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Observation 0639ffaa-ca5d-4a72-a630-27d4940db673 · outbound

This paper cites : 3D-PRNN : Generating shape primitives with recurrent neural networks.

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction : 3D-PRNN : Generating shape primitives with recurrent neural networks

Reference 45

Resolution: unresolved; no resolver link, observed 2026-07-11T05:41:50.671046Z

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