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Paper Citation Record · LEDGER

Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2104.04687.

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

pith.paper-citation-record.v1
2104.04687 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:12:40.516215Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T03:29:29.479882Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8c4be90b-943d-4cff-b205-62f4d56a47b2 · inbound

TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation cites this paper.

TiGDistill-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry Learning Distillation Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 55

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unresolved
no resolver link, observed 2026-08-10T23:12:40.516215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:12:40.516215Z digest=sha256:b9e81e99073f9894328233ae27b44adac57a9bc97b4737679083dcfb4fe1685f

Observation a250914f-d3ef-4f64-a54b-01ec5fdfc6e6 · inbound

The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning cites this paper.

The Devil is in the Details: Simple Remedies for Image-to-LiDAR Representation Learning Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 24

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unresolved
no resolver link, observed 2026-08-10T20:04:16.325560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:04:16.325560Z digest=sha256:3b28ca97a89dafdd5d1dba1f5c81067e6b500056cf9af08cdde41cdaf2c8ee5d

Observation 9f4f886e-07fd-45d9-afb4-98126c62d825 · inbound

Foundational Models for 3D Point Clouds: A Survey and Outlook cites this paper.

Foundational Models for 3D Point Clouds: A Survey and Outlook Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 105

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no resolver link, observed 2026-08-09T22:54:24.677132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:54:24.677132Z digest=sha256:e5e5daedcedd64cad57114d891eaa4aafbc19f51b3bab46a21f9fdf274dd92fa

Observation e44a0215-7d7c-44d4-8666-27562dc9f5cb · inbound

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation cites this paper.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 34

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unresolved
no resolver link, observed 2026-08-06T21:52:45.058273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:52:45.058273Z digest=sha256:96d0789531749ea55177bf86decec8c71014de01c2ac4de14cd980bd2f538891

Observation e9a4a2e1-f3b3-4a9c-b717-89febea07e53 · inbound

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations cites this paper.

Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 52

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unresolved
no resolver link, observed 2026-08-06T19:32:34.713298Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:32:34.713298Z digest=sha256:bbd5261a5292daa95d454298b8c17879a925365fc7df359910a95b50080a3b4b

Observation d0d9424f-6cf3-4b85-8d1b-bf5e39bf080f · inbound

Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding cites this paper.

Multi-modal Multi-task Pre-training for Improved Point Cloud Understanding Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 2021

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unresolved
no resolver link, observed 2026-08-06T14:51:52.741538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:51:52.741538Z digest=sha256:837b9fde07137080886b4424e6454416338f2ba8cc26a6b8db12a86a761fb5e1

Observation 9197fa95-24d5-4be9-b2d0-61a2082c7e31 · inbound

Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation cites this paper.

Domain Adaptation-Based Crossmodal Knowledge Distillation for 3D Semantic Segmentation Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 8

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unresolved
no resolver link, observed 2026-08-05T13:43:55.822844Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T13:43:55.822844Z digest=sha256:414077971801c2e4a3ecc3ef03627fdfe6858fce6983c497c6887118cfc68268

Observation 509cef8b-b9f1-4191-a4e5-05b43eeb3bf1 · inbound

Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis cites this paper.

Bridging the Dimensionality Gap: A Taxonomy and Survey of 2D Vision Model Adaptation for 3D Analysis Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:28:13.972698Z

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.

source=pdf_text observed=2026-05-13T20:26:27.298974Z digest=sha256:25c3aa0ce642d2fc946beabf10715e4999e823daae45683463f96df97767ec0e

Observation b286709b-4a6c-431c-a246-6fb5d5089503 · inbound

Feasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model cites this paper.

Feasibility of Indoor Frame-Wise Lidar Semantic Segmentation via Distillation from Visual Foundation Model Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 11

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metadata mismatch
arxiv_id, observed 2026-05-10T11:35:19.215295Z

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.

source=arxiv_source observed=2026-05-10T04:49:35.318158Z digest=sha256:6e284adaaf152ec6f0e296451543c4d83e4f8b4a1c7895cdc295a8e17be00bc6

Observation 5b6f5e5d-827d-4a33-9e2a-e4c39cb80b75 · inbound

Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection cites this paper.

Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 27

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metadata mismatch
arxiv_id, observed 2026-05-12T06:16:28.786473Z

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.

source=pdf_text observed=2026-05-12T04:25:15.991414Z digest=sha256:1474e4b423b10b53a3b4b200cde6a8ce07a6996650045c3fc4988dcff7fb8d8f

Observation 8d79a1f6-4402-4bd1-91d5-1d3947880498 · inbound

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training cites this paper.

HilDA: Hierarchical Distillation with Diffusion for Advancing Self-Supervised LiDAR Pre-training Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 50

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metadata mismatch
arxiv_id, observed 2026-07-04T03:29:29.483040Z

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.

source=pdf_text observed=2026-06-26T18:06:04.101217Z digest=sha256:b81fcaaa026a1eb300a5e124678ca0140bc61a89814192c26261c1d0c6c56ae5

Observation 273b026d-264f-48eb-8f70-65743e3559bb · inbound

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning cites this paper.

Cross4D-JEPA: Dense Cross-modal Correspondence Distillation for 4D Point Cloud Representation Learning Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-07-02T14:47:03.108630Z

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.

source=pdf_text observed=2026-07-02T14:45:28.839686Z digest=sha256:997c0216f26aeb73c26b68e4c654e930fe16ff9e09b2762ddf47ad6800ebb19f

Observation 933573ac-84db-456b-beb3-d17fb5880473 · inbound

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation cites this paper.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 10

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unresolved
no resolver link, observed 2026-07-14T09:28:01.256510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T09:28:01.256510Z digest=sha256:15dfa8b751c06927940fe186c443c9905a1c42ce6512b7b18d9e065f1ab4d17f

Observation e246d262-3240-41aa-915b-78121bbcc03b · inbound

Vernata: Self-Supervised Learning of LiDAR Point Representations cites this paper.

Vernata: Self-Supervised Learning of LiDAR Point Representations Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-10T18:26:54.504034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:26:54.504034Z digest=sha256:bb3e4fb6b322aa47e02c36ee07c73f9a05b7e3e254448bdf763cae614ff3fc08