Paper Citation Record · LEDGER

PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2310.08586.

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

pith.paper-citation-record.v1
2310.08586 v4

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 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:21:15.438601Z

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

6
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 52bb1065-449b-4e6c-8520-b750a2b2eb46 · inbound

LeAP: Consistent multi-domain 3D labeling using Foundation Models cites this paper.

LeAP: Consistent multi-domain 3D labeling using Foundation Models PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T00:21:15.438601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32e22547-53c6-4ae9-912f-6d3627e935ad · inbound

Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting cites this paper.

Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:06:58.394509Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe3aae7-8eb6-4281-9f19-91c6961eda35 · inbound

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting cites this paper.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:04.397654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dd32b56-564f-46aa-b061-b647ef7917ad · inbound

G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation cites this paper.

G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-03T12:20:46.468030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14570778-60d6-458e-843b-234fdf1f8123 · inbound

Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images cites this paper.

Learning 3D Representations for Spatial Intelligence from Unposed Multi-View Images PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:56:03.529727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3385db3a-5e3b-4121-aa9b-5901134bedcf · inbound

TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework cites this paper.

TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-10T07:01:49.290684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f4329e01-7813-4110-9c4d-0d3d49751ca9 · 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 PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-08T19:24:04.712476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation bd5aa86c-d9d5-4131-b449-0e10cae91209 · inbound

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

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-22T09:51:21.370509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f9ee7d9c-67e7-4094-9760-366a88d79429 · inbound

LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map cites this paper.

LASAR: Towards Spatio-temporal Reasoning with Latent Cognitive Map PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:37:47.955133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cbdae4b4-63f6-4766-a739-e1e031e2f09a · inbound

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation cites this paper.

Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-21T03:49:31.184754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.