Paper Citation Record · LEDGER

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise

As of 8 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2603.06279.

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

pith.paper-citation-record.v1
2603.06279 v2

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T13:54:39.020339Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

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unresolved34
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External citation measurements

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Outbound references

Observation 9108221c-4fff-4044-958b-acbb9cfad727 · outbound

This paper cites V oxDet: Rethinking 3D semantic occupancy prediction as dense object detection,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise V oxDet: Rethinking 3D semantic occupancy prediction as dense object detection,

Reference 1

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Observation 3a5d4a8b-94de-4f34-92e1-72609900ed0c · outbound

This paper cites UniOcc: A unified benchmark for occupancy fore- casting and prediction in autonomous driving,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise UniOcc: A unified benchmark for occupancy fore- casting and prediction in autonomous driving,

Reference 2

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Observation eb91e17b-aff9-419d-8d15-93a3851d2846 · outbound

This paper cites SfmOcc: Vision-based 3D semantic occupancy prediction in urban environments,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise SfmOcc: Vision-based 3D semantic occupancy prediction in urban environments,

Reference 3

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Observation 72dd533f-d098-4854-96bf-d75b6b6d19ac · outbound

This paper cites VPOcc: Exploiting vanishing point for 3D semantic occupancy prediction,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise VPOcc: Exploiting vanishing point for 3D semantic occupancy prediction,

Reference 4

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Observation 1d74352d-5d37-4d83-82db-98a266d43f82 · outbound

This paper cites Quadric- Former: Scene as superquadrics for 3D semantic occupancy predic- tion,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Quadric- Former: Scene as superquadrics for 3D semantic occupancy predic- tion,

Reference 5

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Observation 0e5ddfe9-78d0-4a56-b90c-1824b1c70f2e · outbound

This paper cites Online diffusion-based 3D occupancy prediction at the frontier with proba- bilistic map reconciliation,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Online diffusion-based 3D occupancy prediction at the frontier with proba- bilistic map reconciliation,

Reference 6

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Observation a05b5220-c413-4062-a785-7606a5860903 · outbound

This paper cites H3O: Hyper-efficient 3D occupancy prediction with heterogeneous supervision,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise H3O: Hyper-efficient 3D occupancy prediction with heterogeneous supervision,

Reference 7

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Observation 4c53ecdc-095b-4542-be45-0b72d9559338 · outbound

This paper cites $\alpha$-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise $\alpha$-OCC: Uncertainty-Aware Camera-based 3D Semantic Occupancy Prediction

Reference 8

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Observation cdaf1620-a660-4e34-ac9e-e9990d409e24 · outbound

This paper cites Semantic causality-aware vision-based 3D occupancy prediction,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Semantic causality-aware vision-based 3D occupancy prediction,

Reference 9

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Observation 56e28af0-cc33-4052-b4ff-5659f4f5c097 · outbound

This paper cites ProtoOcc: Ac- curate, efficient 3D occupancy prediction using dual branch encoder- prototype query decoder,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise ProtoOcc: Ac- curate, efficient 3D occupancy prediction using dual branch encoder- prototype query decoder,

Reference 10

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Observation 66973252-944c-461b-93b1-bc9b3c3dd3a1 · outbound

This paper cites AutoOcc: Automatic open-ended semantic occupancy annotation via vision-language guided gaussian splatting,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise AutoOcc: Automatic open-ended semantic occupancy annotation via vision-language guided gaussian splatting,

Reference 11

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Observation 44770fbe-b205-457a-8a65-3bfdbdc92b4e · outbound

This paper cites SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences,

Reference 12

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Observation dd2413f2-a82e-4f0c-841c-48b30e0a3763 · outbound

This paper cites Asymmetric loss functions for noise-tolerant learning: Theory and applications,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Asymmetric loss functions for noise-tolerant learning: Theory and applications,

Reference 13

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Observation a70a7dbc-fef0-4bde-a120-b941af2543f8 · outbound

This paper cites Variation-bounded loss for noise-tolerant learning,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Variation-bounded loss for noise-tolerant learning,

Reference 14

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Observation 015caae1-4375-4dae-b665-cf3c720b08b3 · outbound

This paper cites Active negative loss: A robust framework for learning with noisy labels,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Active negative loss: A robust framework for learning with noisy labels,

Reference 15

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Observation 57efff59-baa6-41fb-a38f-f654dbd31772 · outbound

This paper cites Joint asymmetric loss for learning with noisy labels,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Joint asymmetric loss for learning with noisy labels,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 2a92939f-9779-415f-8276-11e8f4275555 · outbound

This paper cites Continuous review and timely correction: Enhancing the resistance to noisy labels via self-not-true and class-wise distillation,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Continuous review and timely correction: Enhancing the resistance to noisy labels via self-not-true and class-wise distillation,

Reference 17

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Observation b9fd8024-64a1-45d7-b131-010f66b29488 · outbound

This paper cites OCCUQ: Exploring efficient uncertainty quantification for 3D occupancy prediction,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise OCCUQ: Exploring efficient uncertainty quantification for 3D occupancy prediction,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 6492cd62-de59-4ce2-83c6-72a94b622529 · outbound

This paper cites Reliable and calibrated semantic occupancy prediction by hybrid uncertainty learning,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Reliable and calibrated semantic occupancy prediction by hybrid uncertainty learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 7bbb334a-f687-4d7a-ae31-ddd5b0438980 · outbound

This paper cites Particle-based instance-aware semantic occupancy mapping in dynamic environ- ments,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Particle-based instance-aware semantic occupancy mapping in dynamic environ- ments,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 462033de-a3c3-4148-aa94-ae385a495ec1 · outbound

This paper cites HD-CCSOM: Hierarchical and dense collaborative continuous semantic occupancy mapping through label diffusion,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise HD-CCSOM: Hierarchical and dense collaborative continuous semantic occupancy mapping through label diffusion,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation e03fd2ed-e4a7-45d2-aa61-e5227bfc4d44 · outbound

This paper cites Sparse annotation, dense supervision: Unleashing self-training power for occupancy prediction with 2D labels,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Sparse annotation, dense supervision: Unleashing self-training power for occupancy prediction with 2D labels,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 6c6b88ea-6e35-4ab9-80ba-65a4430cc249 · outbound

This paper cites Enhancing generalizability via utilization of unlabeled data for occupancy perception,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Enhancing generalizability via utilization of unlabeled data for occupancy perception,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 4c1921ff-3167-44b2-af79-4af016ce1e7f · outbound

This paper cites Language driven occupancy prediction,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Language driven occupancy prediction,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation dcae28ec-d010-457b-83a5-cbfbb507f732 · outbound

This paper cites nuCraft: Crafting high resolution 3D semantic occupancy for unified 3D scene understanding,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise nuCraft: Crafting high resolution 3D semantic occupancy for unified 3D scene understanding,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-15T13:54:39.020339Z

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Observation 5099f84b-19e6-4563-befc-f4335c21e73e · outbound

This paper cites Knowledge distillation meets label noise learning: Ambiguity-guided mutual label refinery,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Knowledge distillation meets label noise learning: Ambiguity-guided mutual label refinery,

Reference 26

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Observation aefeeec3-7c3b-4fe6-9126-642f8ac569d5 · outbound

This paper cites Robust noisy label learning via two-stream sample distillation,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Robust noisy label learning via two-stream sample distillation,

Reference 27

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Observation 72f925d4-b8f0-4bb1-b245-c9394a2cd1dd · outbound

This paper cites Understanding self-distillation in the pres- ence of label noise,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Understanding self-distillation in the pres- ence of label noise,

Reference 28

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Observation 415828d4-b925-4230-b5e8-7031280f6c0e · outbound

This paper cites Federated learning with extremely noisy clients via negative distillation,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Federated learning with extremely noisy clients via negative distillation,

Reference 29

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Observation 4e427165-98a4-4581-a19d-6a3beef784e1 · outbound

This paper cites Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results,

Reference 30

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Observation 52c5203a-a439-4c33-a431-9f3783849bda · outbound

This paper cites SparseOcc: Rethinking sparse latent representation for vision-based semantic occupancy prediction,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise SparseOcc: Rethinking sparse latent representation for vision-based semantic occupancy prediction,

Reference 31

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Observation d532f1e9-d841-4d7c-82b0-12a7e1a4b21d · outbound

This paper cites SCPNet: Semantic scene completion on point cloud,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise SCPNet: Semantic scene completion on point cloud,

Reference 32

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Observation 24b82c5e-40ae-4153-93fc-6ad750f9f64f · outbound

This paper cites A closer look at memorization in deep networks,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise A closer look at memorization in deep networks,

Reference 33

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Observation ba0b3521-3e4b-417e-9743-d14cb55d58a3 · outbound

This paper cites Deep residual learning for image recognition,.

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise Deep residual learning for image recognition,

Reference 34

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Pith citing papers

No inbound Pith citation observations are available.