Pith. sign in

Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.879864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.879864Z digest=sha256:e581945add7cdb0c34b3b2988b7d1ae80a03ebafc5af8d2e69d983ee5476e4d9

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.912522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.912522Z digest=sha256:e1f243c09890fd7f9e7dd537106173afe2a7c0fa5f6324effc68c2f283575528

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.918070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.918070Z digest=sha256:f2e462f009d2c7a39950b36d30cc5c51bc51e602ab1887cb8c02361591768c40

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.363218Z

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.

source=pdf_text observed=2026-08-08T13:14:13.927157Z digest=sha256:20b70b8ccd7696495925a37783f876354be1dca5f414d9d7ff8ddc1758126b26

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.348472Z

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.

source=pdf_text observed=2026-08-08T13:14:13.931131Z digest=sha256:a179543b59719fb09bdb4a76d06802ab773c114223053eb0380cb1b5937c0d5f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.333756Z

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.

source=pdf_text observed=2026-08-08T13:14:13.935130Z digest=sha256:dadb01c505bd030683cf4f3faccb0d3bfff5da8e581f4c182522714e2342ea3d

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.939425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.939425Z digest=sha256:99fcfca7bd34e582966cbe5d17df84c5063c76313f550097b58136dffb051697

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.944212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.944212Z digest=sha256:ec81e14f7124f55ab65f7d773d9bc0c1c8ec1ffc911d6d5d49ccee7a63390bf6

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.949271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.949271Z digest=sha256:5a5ab97665c4fe4ae8ad300c5c0b03bf3084c1d35232f2b573fbc599a7b9fd93

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.954187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.954187Z digest=sha256:4c0befe62b6f2d416f7b8519ab087260ec5368375ce941d851fc738e20519bae

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.959961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.959961Z digest=sha256:2d9f526967d777839cd4f2fe0935344dc32fdd45634c6c21085ee0347d5732a0

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

Resolution
unresolved
raw_fallback, observed 2026-08-08T13:14:14.319159Z

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.

source=pdf_text observed=2026-08-08T13:14:13.964783Z digest=sha256:b1945f0da2da6b687573859aea9e0a488c3a4ec08d6085a308eb0a9b82c0ca78

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

Resolution
unresolved
raw_fallback, observed 2026-08-08T13:14:14.304636Z

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.

source=pdf_text observed=2026-08-08T13:14:13.969240Z digest=sha256:1710448a5abe8716c355bdfcb9a9ca9c48d98ffb9010dac585995e4462984ffd

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

Resolution
unresolved
raw_fallback, observed 2026-08-08T13:14:14.288399Z

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.

source=pdf_text observed=2026-08-08T13:14:13.974059Z digest=sha256:e4eface7dc7fbe927e9df2e2a35085f78623c3aa8dd12782cdf7a4f9891dadcf

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.273931Z

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.

source=pdf_text observed=2026-08-08T13:14:13.978747Z digest=sha256:08332464dea002d762c51fee3e54a1020bf57acdcbf49d15e9e20559959655bb

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

Resolution
unresolved
raw_fallback, observed 2026-08-08T13:14:14.258437Z

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.

source=pdf_text observed=2026-08-08T13:14:13.984116Z digest=sha256:e3b7cc3dfa66821d007a78b6a882140c72d7b68d333135298515533ef0693624

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.242579Z

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.

source=pdf_text observed=2026-08-08T13:14:13.988754Z digest=sha256:9ea61e03d0fc5f3e3368b0c5510118a50eaca7471721316bc153ccbcfbb80416

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.226034Z

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.

source=pdf_text observed=2026-08-08T13:14:13.992401Z digest=sha256:85edda3748f155889e81d75f4c05d98f7f217f3f594f207dcfb7553449118ea7

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.896531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.896531Z digest=sha256:cde435d7d3ac3b868168f38dc242431b7d43da6ca29581d54da16f3a87926c9e

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.922655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.922655Z digest=sha256:290e2857634faecd88a99111918c0ef6e86ed76edab7e4707a831709bf9bd51d

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.885960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.885960Z digest=sha256:639a41d4e022374d6099239bca786d32da42a678e24bc5b9094ebb3fd4a77353

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.901873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.901873Z digest=sha256:7766452e2748e3aa4b1adf9eee2aac8e17670a45d7fc896ec33947cafa498ef8

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

Resolution
unresolved
no resolver link, observed 2026-08-08T13:14:13.891244Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T13:14:13.891244Z digest=sha256:24bedd8f1cefdca8ad05014e989135b0a947e765f754d3a3ef76bf88124cc9d3

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-08T13:14:14.377535Z

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.

source=pdf_text observed=2026-08-08T13:14:13.906955Z digest=sha256:f8d330e27dfd16029b08959db463a8aed28e5de9eac38d2c3d2ae91d00793d23

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

Resolution
unresolved
no resolver link, observed 2026-08-05T06:04:22.334747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T06:04:22.334747Z digest=sha256:886355b5991d0535a119072e7b1f852a5719c68acb7cc62834d8dbb0b4c6ec8b

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

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

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.

source=pdf_text observed=2026-05-17T05:26:34.859975Z digest=sha256:96b4c9c834130cf09a0981d065b998a632262dd103c588c7c933cbd016acc225

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

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

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.

source=pdf_text observed=2026-05-10T17:08:10.442655Z digest=sha256:c43c2aabb807b3ffc5931dc98bea86788de0c09cbd0c89e2902f57c1c0f5c2ef