Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2607.10762.

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

pith.paper-citation-record.v1
2607.10762 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-14T09:28:01.256510Z

measured 36 of 36 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

36 of 36 outbound references displayed

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verified fuzzy0
unresolved36
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 84766c98-e458-41c7-a20d-5ff7ad054f69 · outbound

This paper cites Semantickitti: A dataset for seman- tic scene understanding of lidar sequences,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Semantickitti: A dataset for seman- tic scene understanding of lidar sequences,

Reference 1

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Observation a665f069-c3e7-4959-8fe6-85eca71cbfdf · outbound

This paper cites WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environ- ments,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation WildScenes: A Benchmark for 2D and 3D Semantic Segmentation in Large-scale Natural Environ- ments,

Reference 2

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Observation 8a9d1dd0-2a03-4e6e-9bb5-df98ebdf5157 · outbound

This paper cites Deep evidential uncertainty esti- mation for semantic segmentation under out-of-distribution obstacles,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Deep evidential uncertainty esti- mation for semantic segmentation under out-of-distribution obstacles,

Reference 3

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Observation 9ec201de-71fe-43f7-a1a1-ba14993259ef · outbound

This paper cites Suma++: Efficient lidar-based semantic slam,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Suma++: Efficient lidar-based semantic slam,

Reference 4

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

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Observation 14083451-306d-44c6-8654-e5993b5e2c2d · outbound

This paper cites Pointmoseg: Sparse tensor-based end-to- end moving-obstacle segmentation in 3-d lidar point clouds for au- tonomous driving,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Pointmoseg: Sparse tensor-based end-to- end moving-obstacle segmentation in 3-d lidar point clouds for au- tonomous driving,

Reference 5

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Observation 9ecb6840-775c-42a9-a091-edb0ed5f0681 · outbound

This paper cites Theia: Distilling diverse vision foundation models for robot learning,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Theia: Distilling diverse vision foundation models for robot learning,

Reference 6

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

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Observation d13e4282-f684-4db9-bc1b-a354b5bf0f61 · outbound

This paper cites Pair-VPR: Place-Aware Pre-Training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Pair-VPR: Place-Aware Pre-Training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers,

Reference 7

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Observation 14e0a7d5-d4a8-4bf5-9e23-ee7fb7ea409a · outbound

This paper cites ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation ImLPR: Image-based LiDAR Place Recognition using Vision Foundation Models,

Reference 8

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Observation 6625ddc3-f294-42d4-81d9-27277dd208a9 · outbound

This paper cites Label-efficient lidar semantic segmen- tation with 2d-3d vision transformer adapters,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Label-efficient lidar semantic segmen- tation with 2d-3d vision transformer adapters,

Reference 9

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

This paper cites Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining.

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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Observation 3034f2f3-da9a-428f-9ca3-09bb5083337b · outbound

This paper cites Three pillars improving vision foundation model distillation for lidar,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Three pillars improving vision foundation model distillation for lidar,

Reference 11

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Observation ac8a2b7e-43e8-4bef-828b-dde9832f38f0 · outbound

This paper cites Image-to-lidar self-supervised distillation for autonomous driving data,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Image-to-lidar self-supervised distillation for autonomous driving data,

Reference 12

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Observation 509029e5-5c15-408d-8345-6cfe421a8bbb · outbound

This paper cites Segment any point cloud sequences by distilling vision foundation models,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Segment any point cloud sequences by distilling vision foundation models,

Reference 13

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

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Observation 2e6b1bdb-b57b-44d0-bc9c-9884cf0204ac · outbound

This paper cites Cleverdistiller: Simple and spatially consistent cross-modal distillation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Cleverdistiller: Simple and spatially consistent cross-modal distillation,

Reference 14

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

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Observation d13a5021-11e1-4b1c-8fe9-6c3073ddc83b · outbound

This paper cites Cross-modal self-supervised learning with effective contrastive units for lidar point clouds,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Cross-modal self-supervised learning with effective contrastive units for lidar point clouds,

Reference 15

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Observation bf63f9fe-9f02-47cc-8329-ed8fdb2fa157 · outbound

This paper cites Point-PNG: Conditional Pseudo- Negatives Generation for Point Cloud Pre-Training,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Point-PNG: Conditional Pseudo- Negatives Generation for Point Cloud Pre-Training,

Reference 16

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

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Observation 533425b7-2a00-4ee6-9165-535a2b9f574a · outbound

This paper cites Expert-enhanced masked point modeling for point cloud self-supervised learning,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Expert-enhanced masked point modeling for point cloud self-supervised learning,

Reference 17

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

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Observation d70c9a7b-cc0b-48c1-8d7c-39940f52d2e8 · outbound

This paper cites Clip2scene: Towards label-efficient 3d scene understanding by clip,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Clip2scene: Towards label-efficient 3d scene understanding by clip,

Reference 18

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

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Observation 218e7b32-43e6-4fe9-ad38-df5bdbbbe30d · outbound

This paper cites Largead: Large-scale cross-sensor data pretraining for autonomous driving,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Largead: Large-scale cross-sensor data pretraining for autonomous driving,

Reference 19

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

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Observation 0a01c858-be10-4ae0-a197-680324f599c6 · outbound

This paper cites Do vision transformers see like convolutional neural networks?.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Do vision transformers see like convolutional neural networks?

Reference 20

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

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Observation d64bec77-e9cd-4e13-a851-3011640d1105 · outbound

This paper cites One-for-all: Bridge the gap between hetero- geneous architectures in knowledge distillation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation One-for-all: Bridge the gap between hetero- geneous architectures in knowledge distillation,

Reference 21

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

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Observation 6611b169-bea6-4dd2-9108-c08d06455cce · outbound

This paper cites Cumulative spatial knowledge distillation for vision transformers,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Cumulative spatial knowledge distillation for vision transformers,

Reference 22

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

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Observation b4aac3f5-aa83-42d6-a74a-5a7427f19d2d · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation nuscenes: A multimodal dataset for autonomous driving,

Reference 23

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

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Observation 5f56e3a3-eaa2-4585-ac8c-6ef1d0461582 · outbound

This paper cites Pandaset: Advanced sensor suite dataset for autonomous driving,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Pandaset: Advanced sensor suite dataset for autonomous driving,

Reference 24

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

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Observation ba520142-60d6-490c-be60-5f7c1bae1fc5 · outbound

This paper cites Scalability in perception for au- tonomous driving: Waymo open dataset,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Scalability in perception for au- tonomous driving: Waymo open dataset,

Reference 25

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Observation 4d14490b-02a9-4195-8092-6cfded02042d · outbound

This paper cites Improving multimodal distillation for 3d semantic segmentation under domain shift,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Improving multimodal distillation for 3d semantic segmentation under domain shift,

Reference 26

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Observation d047f6fb-4fd8-40fa-a464-99e079c996e6 · outbound

This paper cites Self-supervised image-to-point distil- lation via semantically tolerant contrastive loss,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Self-supervised image-to-point distil- lation via semantically tolerant contrastive loss,

Reference 27

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Observation 83b2f537-2ae9-47e2-9416-e8a5c4e0546f · outbound

This paper cites Minimal-entropy correlation alignment for unsupervised deep domain adaptation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Minimal-entropy correlation alignment for unsupervised deep domain adaptation,

Reference 28

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Observation c8a5eb0d-30f4-48e7-afad-b31948593f9c · outbound

This paper cites Cosmix: Compositional semantic mix for domain adaptation in 3d lidar segmentation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Cosmix: Compositional semantic mix for domain adaptation in 3d lidar segmentation,

Reference 29

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

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Observation fd66f901-46d7-49f5-8fed-3b737a633fdf · outbound

This paper cites Exploiting the complementarity of 2d and 3d networks to address domain-shift in 3d semantic seg- mentation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Exploiting the complementarity of 2d and 3d networks to address domain-shift in 3d semantic seg- mentation,

Reference 30

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Observation 4674341f-3cc0-4e87-8db2-081a9138a3e2 · outbound

This paper cites Learning to adapt sam for segmenting cross- domain point clouds,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Learning to adapt sam for segmenting cross- domain point clouds,

Reference 31

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Observation f02aebbb-1662-4faa-9d33-725c50509d9d · outbound

This paper cites Using a waffle iron for automotive point cloud semantic segmentation,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Using a waffle iron for automotive point cloud semantic segmentation,

Reference 32

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Observation 27cca281-1886-4e88-b1f8-d9780eb9585f · outbound

This paper cites DINOv2: Learning robust visual features without supervision,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation DINOv2: Learning robust visual features without supervision,

Reference 33

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Observation d4cedd7d-ac6e-45fb-b8d6-b1a60f635c6d · outbound

This paper cites Dune: Distilling a universal encoder from heterogeneous 2d and 3d teachers,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Dune: Distilling a universal encoder from heterogeneous 2d and 3d teachers,

Reference 34

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Observation 05851c56-99ec-4c69-a5dd-15cbf6dc5823 · outbound

This paper cites Robo3D: Towards robust and reliable 3d perception against corruptions,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation Robo3D: Towards robust and reliable 3d perception against corruptions,

Reference 35

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Observation e9e074c9-5dd7-423e-b98b-971a21d86c50 · outbound

This paper cites 4d contrastive superflows are dense 3d representation learners,.

TOLiD: Bridging the Architecture Gap in Vision Foundation Model to LiDAR Pretraining via Token Lifting for Distillation 4d contrastive superflows are dense 3d representation learners,

Reference 36

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

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Pith citing papers

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