Paper Citation Record · LEDGER

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation

As of 11 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 1 inbound Pith citation observation for arXiv:2508.14036.

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

pith.paper-citation-record.v1
2508.14036 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:49:02.797225Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T19:52:50.074000Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T22:25:51.764052Z

Reference resolution

42 of 42 outbound references displayed

verified exact4
verified fuzzy26
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fb68b2e0-f4d4-4878-918c-7c74e23445bf · outbound

This paper cites Satr: Zero-shot semantic segmentation of 3d shapes, 2023.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Satr: Zero-shot semantic segmentation of 3d shapes, 2023

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:11.450114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 34b12f46-51bf-4986-b4b9-3c4ad48ad6f4 · outbound

This paper cites Seg- ment anything in 3d with radiance fields, 2024.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Seg- ment anything in 3d with radiance fields, 2024

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:11.098141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f00f552-8084-4c69-a1f9-fa9eddf7d3ef · outbound

This paper cites A benchmark for 3D mesh segmentation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation A benchmark for 3D mesh segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:10.792354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 12c5d1f4-036a-426e-9ad9-37484e31b36d · outbound

This paper cites 3d part seg- mentation via geometric aggregation of 2d visual features.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation 3d part seg- mentation via geometric aggregation of 2d visual features

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:10.449111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b18d572d-eb5e-4b20-b214-2375b601ddc9 · outbound

This paper cites Meshcnn: a network with an edge.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Meshcnn: a network with an edge

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:10.107435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7aa812e6-5353-48f8-afa7-a77d0cc1f7d2 · outbound

This paper cites Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:49:03.860067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b7c3f61-84eb-4f6d-bc00-5e93a1566d8c · outbound

This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:57.905373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a0b72c8-55b8-49b8-a63c-857652576230 · outbound

This paper cites Grounded language-image pre-training.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Grounded language-image pre-training

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:09.791398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b1166fdb-38b2-4486-beeb-76df1bcba8ea · outbound

This paper cites Laplacian mesh transformer: Dual attention and topology aware net- work for 3d mesh classification and segmentation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Laplacian mesh transformer: Dual attention and topology aware net- work for 3d mesh classification and segmentation

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:09.494035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2a814535-d5fc-44e5-a0e9-a437782fa92e · outbound

This paper cites Pointcnn: Convolution on x-transformed points.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Pointcnn: Convolution on x-transformed points

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:09.155844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0d0baa3e-14f8-475e-97fb-2d6e985161fe · outbound

This paper cites Triposg: High- fidelity 3d shape synthesis using large-scale rectified flow models, 2025.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Triposg: High- fidelity 3d shape synthesis using large-scale rectified flow models, 2025

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:08.789201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 098365f6-a088-4197-a1bb-79bdabb17582 · outbound

This paper cites Partslip: Low-shot part seg- mentation for 3d point clouds via pretrained image-language models.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Partslip: Low-shot part seg- mentation for 3d point clouds via pretrained image-language models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:08.495237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9ba4be0e-cc77-4d6e-8145-2d7dd82b97a6 · outbound

This paper cites Partslip: Low-shot part seg- mentation for 3d point clouds via pretrained image-language models, 2023.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Partslip: Low-shot part seg- mentation for 3d point clouds via pretrained image-language models, 2023

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:08.157273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation aea857e2-268e-49e6-8c85-a411abc09811 · outbound

This paper cites PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation PARTFIELD: Learning 3D Feature Fields for Part Segmentation and Beyond

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:59.042706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe9e762e-d4b2-47a2-a560-9f19a7a67b3c · outbound

This paper cites SANeRF-HQ: Segment Anything for NeRF in High Quality.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation SANeRF-HQ: Segment Anything for NeRF in High Quality

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:49:03.661306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 19e6cf93-0cf7-4889-919e-3e7fd04f94d5 · outbound

This paper cites Find any part in 3d, 2025.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Find any part in 3d, 2025

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:07.879065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f15f2b2f-4686-4fd5-b710-0e59df186289 · outbound

This paper cites Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 3d object understanding.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 3d object understanding

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:07.639149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 85ad4363-86f8-4612-ac69-555d508947d0 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation DINOv2: Learning Robust Visual Features without Supervision

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:59.665899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d3f6726-586c-4d9b-84c6-1d9a11da3c2c · outbound

This paper cites Better Call SAL: Towards Learning to Segment Anything in Lidar.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Better Call SAL: Towards Learning to Segment Anything in Lidar

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T18:48:59.836892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f896f5b5-f0e3-482c-a1e3-bcb2cdaeb4e1 · outbound

This paper cites Openscene: 3d scene understanding with open vocabularies.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Openscene: 3d scene understanding with open vocabularies

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:07.288315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 46f098d5-62b1-44bb-b7a0-584b062df8a4 · outbound

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

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:07.017526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8d8ace50-0b3d-4dae-8a8e-526ec7a92e71 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Learn- ing transferable visual models from natural language super- vision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:00.298395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc6ba85d-3faa-46de-a71d-cc1c47103286 · outbound

This paper cites Neural shape diameter function for efficient mesh segmentation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Neural shape diameter function for efficient mesh segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:06.659099Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8087cc8d-652b-4385-a03f-02e721fd5ada · outbound

This paper cites Consis- tent mesh partitioning and skeletonisation using the shape diameter function.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Consis- tent mesh partitioning and skeletonisation using the shape diameter function

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:06.327785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5b058fdb-14f1-4789-9121-482e298e0d54 · outbound

This paper cites Segment Any Mesh.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Segment Any Mesh

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:00.647957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e44a4ec-16a0-4418-83be-aef384d177e1 · outbound

This paper cites Segment any mesh: Zero-shot mesh part segmentation via lifting segment anything 2 to 3d, 2024.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Segment any mesh: Zero-shot mesh part segmentation via lifting segment anything 2 to 3d, 2024

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:06.102873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2ccdf9db-2e0d-427b-936e-fdf111cd4223 · outbound

This paper cites Segment any mesh, 2025.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Segment any mesh, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:05.860140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6983f51f-bd89-40bf-be2f-05c4e0db987f · outbound

This paper cites Generating part-aware editable 3d shapes without 3d supervision.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Generating part-aware editable 3d shapes without 3d supervision

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:05.611769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a1d79cd8-2f4d-4f42-bc5e-44cf8a1b648e · outbound

This paper cites PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation PartDistill: 3D Shape Part Segmentation by Vision-Language Model Distillation

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:49:03.399771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b6aa5d20-7482-4476-9314-35ff15c27515 · outbound

This paper cites Open-vocabulary part-based grasping.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Open-vocabulary part-based grasping

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:01.397325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7bf17116-9be9-47c2-9268-dd790fd41ddb · outbound

This paper cites Coseg: Cognitively inspired unsupervised generic event segmenta- tion.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Coseg: Cognitively inspired unsupervised generic event segmenta- tion

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:05.328717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b52db02e-efd7-42c7-84d8-456430469860 · outbound

This paper cites SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation SAMPro3D: Locating SAM Prompts in 3D for Zero-Shot Instance Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:01.658971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d9e0f2f-1b93-4697-bf75-57eff36c2aa6 · outbound

This paper cites ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation ZeroPS: High-quality Cross-modal Knowledge Transfer for Zero-Shot 3D Part Segmentation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:49:03.066818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a2badeac-954c-43f0-a7ae-cedccec7566f · outbound

This paper cites SAM3D: Segment Anything in 3D Scenes.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation SAM3D: Segment Anything in 3D Scenes

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:01.907526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59dfa1e0-700a-4c98-9fed-63dcdf9b1ee6 · outbound

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

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation SAMPart3D: Segment Any Part in 3D Objects

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:02.022186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 118eeb36-c641-4e03-a920-1c637b226b12 · outbound

This paper cites Holopart: Generative 3d part amodal segmentation, 2025.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Holopart: Generative 3d part amodal segmentation, 2025

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:05.056019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02b285f8-d1e8-4b61-91b2-77c28ff0d224 · outbound

This paper cites Point transformer.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Point transformer

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:04.776662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c48d370d-a30a-4cff-beb2-8cd1dadfea20 · outbound

This paper cites Meshsegmenter: Zero-shot mesh semantic segmentation via texture synthesis.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Meshsegmenter: Zero-shot mesh semantic segmentation via texture synthesis

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:04.529747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8a4c7a51-1193-40d5-98d9-78c176b1204e · outbound

This paper cites Serf: Fine-grained interactive 3d segmentation and editing with radiance fields, 2024.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Serf: Fine-grained interactive 3d segmentation and editing with radiance fields, 2024

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:04.279390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 696c447c-5445-4347-b7cc-fcc08f17ccb4 · outbound

This paper cites PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation PartSLIP++: Enhancing Low-Shot 3D Part Segmentation via Multi-View Instance Segmentation and Maximum Likelihood Estimation

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:02.571294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1ca8109-4897-4656-89c9-497ad75722a1 · outbound

This paper cites Point-SAM: Promptable 3D Segmentation Model for Point Clouds.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Point-SAM: Promptable 3D Segmentation Model for Point Clouds

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T18:49:02.680650Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c28b3c3-dc01-41cc-af00-4c074e644bb6 · outbound

This paper cites Point- clip v2: Prompting clip and gpt for powerful 3d open-world learning.

GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation Point- clip v2: Prompting clip and gpt for powerful 3d open-world learning

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:49:04.070560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

Observation a17e097a-f6fd-447c-a3a1-841c22be70d8 · inbound

Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation cites this paper.

Part-Level 3D Gaussian Vehicle Generation with Joint and Hinge Axis Estimation GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentation

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:25:51.767452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.