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Paper Citation Record · LEDGER

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

As of 23 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 3 inbound Pith citation observations for arXiv:2502.07309.

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

pith.paper-citation-record.v1
2502.07309 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:14:13.992401Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T06:04:22.334747Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T05:29:05.000892Z

Reference resolution

24 of 24 outbound references displayed

  • verified exact0
  • verified fuzzy7
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bebd62a2-1afe-4e4d-99fb-2895ad40def0 · outbound

This paper cites OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow

Reference 1

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source=pdf_text observed=2026-08-08T13:14:13.879864Z digest=sha256:bef6943b76d22ed62a18767a85ebc5966f4843592ed09a576ec66085f1a7a82e

Observation bbf8b656-c362-4ef5-ac0e-b174bc67c342 · outbound

This paper cites FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation

Reference 7

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source=pdf_text observed=2026-08-08T13:14:13.912522Z digest=sha256:0d59dafbde6b07c44288c00cfc007b7e27ddf868c99491635658981596eb3926

Observation 9b8df38a-45df-4432-9e2a-b816bd12b633 · outbound

This paper cites AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

Reference 8

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source=pdf_text observed=2026-08-08T13:14:13.918070Z digest=sha256:893fefe8597113cf18756aad8ac49ba838b5197f843a50b7031c5a22b1f7506d

Observation 166e6137-1c4c-4e9a-9827-fa4b6fdf9b31 · outbound

This paper cites Renderocc: Vision-centric 3d occupancy prediction with 2d ren- dering supervision.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Renderocc: Vision-centric 3d occupancy prediction with 2d ren- dering supervision

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T13:14:13.927157Z digest=sha256:53deec0212ef726afe097fa5d7e9cff05cdd4cacf0d10027f1b1b71c61d729a4

Observation e1c93daa-569c-4533-b7d9-5caa3d964a2f · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d

Reference 11

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T13:14:13.931131Z digest=sha256:3d19539c390a52c39745884e9ad699b0f717746fdc6dbadcd39dc347e957713a

Observation a174020c-1f29-4ddd-a4b6-a0f8980245fe · outbound

This paper cites Scene as occupancy.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Scene as occupancy

Reference 12

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source=pdf_text observed=2026-08-08T13:14:13.935130Z digest=sha256:476a6c5eda0432cadd7d8adfaa090c4bdf81f1b1dc69937dcd78ef90f5819219

Observation 479b6d9f-8ef3-4400-9330-740c5011a30c · outbound

This paper cites OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving

Reference 13

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source=pdf_text observed=2026-08-08T13:14:13.939425Z digest=sha256:67e7dd1592a657b11cec1d32e7d6c89de52bf389ad297850d2a8ae67c2b68676

Observation 148fd0c3-f10e-4855-ae19-bd9de82b9b64 · outbound

This paper cites DRINet++: Efficient Voxel-as-point Point Cloud Segmentation.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving DRINet++: Efficient Voxel-as-point Point Cloud Segmentation

Reference 14

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source=pdf_text observed=2026-08-08T13:14:13.944212Z digest=sha256:0917e99025c444faab6f3e39ab583fd89bdb46a66cc933d0c0b8e9e8ebbecbca

Observation 1fea8bf9-7550-4fb4-b3c1-84a561860422 · outbound

This paper cites Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

Reference 15

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source=pdf_text observed=2026-08-08T13:14:13.949271Z digest=sha256:293ace3aeb0278c64127a574828ff93d86e2a9013f16410e608ca8831fa6e85e

Observation ef9642f4-9fba-4967-9025-cf9a069121f5 · outbound

This paper cites OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments

Reference 16

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source=pdf_text observed=2026-08-08T13:14:13.954187Z digest=sha256:7e00952a8117c768f8a00663caac23a79dc3a786f7755518949e6e1e695dceb8

Observation c550d25b-521c-4e21-825c-64d889bdfbf9 · outbound

This paper cites MonoOcc: Digging into Monocular Semantic Occupancy Prediction.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving MonoOcc: Digging into Monocular Semantic Occupancy Prediction

Reference 17

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source=pdf_text observed=2026-08-08T13:14:13.959961Z digest=sha256:dee49358b67b6e11e7b09656e5dd70a316ed377acc4015f234de1e268d2bb31c

Observation 100a2ef0-21c1-4c67-8f8e-6bf1697493cf · outbound

This paper cites an unresolved cited work.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Unresolved cited work

Reference 18

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Observation 837e8178-c72b-47b4-a355-f556cad04170 · outbound

This paper cites an unresolved cited work.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-08T13:14:13.969240Z digest=sha256:85f41ce9dbca2162e33edeb9c56d423a071812c02143942d16baf3a7111de414

Observation 8e230269-96eb-470e-af4c-066e0bc473eb · outbound

This paper cites an unresolved cited work.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Unresolved cited work

Reference 20

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source=pdf_text observed=2026-08-08T13:14:13.974059Z digest=sha256:ee98f3e30475bac03004f69b72731443c1bda7db5c807806e49d179a54b54c78

Observation 1c5bb530-4f47-45ed-93cf-366bc13df1aa · outbound

This paper cites While OccFlowNet outperforms SparseOcc in the mIoU metric with 33.86 over 30.90, its performance notably lags behind SparseOcc in terms of RayIoU.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving While OccFlowNet outperforms SparseOcc in the mIoU metric with 33.86 over 30.90, its performance notably lags behind SparseOcc in terms of RayIoU

Reference 21

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source=pdf_text observed=2026-08-08T13:14:13.978747Z digest=sha256:c66b8f29daf5021138905883b0cd5cca0fb447769c8f384f9a7706c29c0ff7f5

Observation e5572677-85f8-4e8c-88bc-5e9ee1d60936 · outbound

This paper cites an unresolved cited work.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-08T13:14:13.984116Z digest=sha256:0b4018ac9b4ba3ba81a1f4c741a4ecab49e11991a11eb784be5b258937d400a9

Observation 7849010c-1863-45a1-81bd-992842bd3938 · outbound

This paper cites The red boxes highlight fine-grained details of the 3D occupancy predictions and the ground truth, while the orange boxes mark holistic structure of an area within the scene.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving The red boxes highlight fine-grained details of the 3D occupancy predictions and the ground truth, while the orange boxes mark holistic structure of an area within the scene

Reference 23

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source=pdf_text observed=2026-08-08T13:14:13.988754Z digest=sha256:43879fe975d42904235772ac46af4b8a500282ab5253d342e651f5cb1caff6aa

Observation 481aae12-8a8f-45a6-843b-5c76d2abfadf · outbound

This paper cites However, while this approach may lead to higher mIoU scores, its predictions for occluded regions are chaotic, indicating a lack of true understanding of the scene structure.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving However, while this approach may lead to higher mIoU scores, its predictions for occluded regions are chaotic, indicating a lack of true understanding of the scene structure

Reference 24

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source=pdf_text observed=2026-08-08T13:14:13.992401Z digest=sha256:dadf89ed402b7d0a95301d53ebdf07c0aeea7687906d8274b47d87a7cb4f7728

Observation 39b73ce5-362c-4a92-ad16-b986708da699 · outbound

This paper cites GAIA-1: A Generative World Model for Autonomous Driving.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving GAIA-1: A Generative World Model for Autonomous Driving

Reference 2018

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source=pdf_text observed=2026-08-08T13:14:13.896531Z digest=sha256:4f1708126cdc5626da12ae897cbf5e505b56280bcf2b02ab89865836fec1e8ff

Observation 78470dee-ff36-4a56-b744-74baea27e871 · outbound

This paper cites Fully Sparse 3D Occupancy Prediction.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Fully Sparse 3D Occupancy Prediction

Reference 2020

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source=pdf_text observed=2026-08-08T13:14:13.922655Z digest=sha256:ee8e0474e2bf9a79738ff477778be5d7be19950e60f87733c7c7e8cc5d68f6a0

Observation 26c82d9e-1e31-41d8-8410-00cd95eee709 · outbound

This paper cites MagicDrive: Street View Generation with Diverse 3D Geometry Control.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving MagicDrive: Street View Generation with Diverse 3D Geometry Control

Reference 2021

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source=pdf_text observed=2026-08-08T13:14:13.885960Z digest=sha256:77f839d3d4c9dd772faa8cf10acfbdeda17a8eab12cf46b7fa99673de0d244c8

Observation 97972ea8-1392-4fab-8402-f401d3498076 · outbound

This paper cites BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View

Reference 2022

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source=pdf_text observed=2026-08-08T13:14:13.901873Z digest=sha256:672e7ed5a2f9a5759daf5318ed4fac949caa4d95382da3c105d16aaaecc304e8

Observation 2b6908dc-d7b2-4c34-bc2a-1a0c0732bdb3 · outbound

This paper cites World Models.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving World Models

Reference 2023

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source=pdf_text observed=2026-08-08T13:14:13.891244Z digest=sha256:522c02a22aa0f54d9bd589488598c65f33a3422342fa2cfe8e9755ef98b86da5

Observation 3313c580-c37e-4828-9a83-c122c8898a74 · outbound

This paper cites Dif- ferentiable raycasting for self-supervised occupancy forecasting.

Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving Dif- ferentiable raycasting for self-supervised occupancy forecasting

Reference 2024

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-08T13:14:13.906955Z digest=sha256:ae912463b71802a457d1ce0280b8c02887d9dcec75e15efb6351e0e22bd92ed2

Pith citing papers

Observation 7a9959ca-0ac7-4c55-b07d-8d9e1fff7706 · inbound

3D and 4D World Modeling: A Survey cites this paper.

3D and 4D World Modeling: A Survey Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Reference 133

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source=pdf_text observed=2026-08-05T06:04:22.334747Z digest=sha256:825b6bebcd054c34add754c49385c85f23f0b696ef6e20bad283eb86967f36be

Observation 33817271-c679-4302-87cd-5f2f6f1caf1c · inbound

SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model cites this paper.

SparseWorld-TC: Trajectory-Conditioned Sparse Occupancy World Model Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Reference 18

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arxiv_id, observed 2026-05-17T05:29:05.002759Z

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source=pdf_text observed=2026-05-17T05:26:34.859975Z digest=sha256:b459b395c8dc7a9b89e7da7c7606b669070e313264e0d19d5aa648f6452e3571

Observation 7a0c813a-0318-4528-98c3-a627bad0bf58 · inbound

Learning Vision-Language-Action World Models for Autonomous Driving cites this paper.

Learning Vision-Language-Action World Models for Autonomous Driving Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous Driving

Reference 37

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arxiv_id, observed 2026-05-11T07:31:00.860516Z

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source=pdf_text observed=2026-05-10T17:08:10.442655Z digest=sha256:23c03ac65e2778f880aa7d998fd08bd3dfac8aacac4886d03200b90a570c6963