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Paper Citation Record · LEDGER

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking

As of 8 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 0 inbound Pith citation observations for arXiv:2507.19908.

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

pith.paper-citation-record.v1
2507.19908 v1

Coverage vector

measured 64 of 64 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:56:33.382609Z

measured 64 of 64 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

64 of 64 outbound references displayed

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  • verified fuzzy51
  • unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d72ef24f-7a2b-4ab1-9c21-5c2307709d93 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking nuscenes: A multi- modal dataset for autonomous driving

Reference 1

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Observation b24f4778-4da6-44bf-ba61-fdd87d671f72 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking ShapeNet: An Information-Rich 3D Model Repository

Reference 2

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Observation febaea86-d2ab-460e-aeb1-5bb13598f651 · outbound

This paper cites Joint classification and regression for visual tracking with fully convolutional siamese networks.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Joint classification and regression for visual tracking with fully convolutional siamese networks

Reference 3

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Observation 969c7058-5dae-4091-bdcb-762a84a196aa · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 4

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Source-reported events for the cited work

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Observation a2d0b9a5-a9cb-48fa-9dcc-89fd9970cacb · outbound

This paper cites Bert: Pre-training of deep bidirectional trans- formers for language understanding.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Bert: Pre-training of deep bidirectional trans- formers for language understanding

Reference 5

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Source-reported events for the cited work

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Observation 2a5f7272-f11c-4855-ac57-797ada250f6c · outbound

This paper cites An image is worth 16x16 words: Trans- formers for image recognition at scale.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking An image is worth 16x16 words: Trans- formers for image recognition at scale

Reference 6

Resolution
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Observation 587e65c7-d84e-4988-8704-06be6757db90 · outbound

This paper cites 3d-siamrpn: An end-to-end learning method for real-time 3d single object tracking using raw point cloud.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking 3d-siamrpn: An end-to-end learning method for real-time 3d single object tracking using raw point cloud

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5b1d91b1-9982-4e84-9702-900d907448db · outbound

This paper cites CLIP-Adapter: Better Vision-Language Models with Feature Adapters.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking CLIP-Adapter: Better Vision-Language Models with Feature Adapters

Reference 8

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Source-reported events for the cited work

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Observation 8454964d-d7c0-4491-93fc-6f3d673afae0 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 97836163-8ee4-435a-a32d-3857002932c9 · outbound

This paper cites Lever- aging shape completion for 3d siamese tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Lever- aging shape completion for 3d siamese tracking

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 5fbee345-be9a-4bde-8059-e5241da8b6be · outbound

This paper cites Parameter-efficient transfer learning for nlp.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Parameter-efficient transfer learning for nlp

Reference 11

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Source-reported events for the cited work

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Observation b6c62bb7-729a-4a04-ac78-7f41552fda06 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Lora: Low-rank adaptation of large language models

Reference 12

Resolution
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Source-reported events for the cited work

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Observation 6e57feda-e6da-4813-a7f1-a585260fb4a9 · outbound

This paper cites Clip2point: Transfer clip to point cloud classifica- tion with image-depth pre-training.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Clip2point: Transfer clip to point cloud classifica- tion with image-depth pre-training

Reference 13

Resolution
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Source-reported events for the cited work

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Observation 8a47b0b6-4beb-4693-add0-027896d6fb8c · outbound

This paper cites 3d siamese voxel-to-bev tracker for sparse point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking 3d siamese voxel-to-bev tracker for sparse point clouds

Reference 14

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Source-reported events for the cited work

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Observation 9cfcc572-51cf-464a-af86-d222f23aaf44 · outbound

This paper cites 3d siamese transformer network for single object tracking on point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking 3d siamese transformer network for single object tracking on point clouds

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation c43aa39f-11d9-4780-b4a7-55158b5d7ff6 · outbound

This paper cites Vi- sual prompt tuning.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Vi- sual prompt tuning

Reference 16

Resolution
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Source-reported events for the cited work

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Observation 63372031-4381-474d-ba01-40a71ad31a47 · outbound

This paper cites Maple: Multi-modal prompt learning.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Maple: Multi-modal prompt learning

Reference 17

Resolution
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Source-reported events for the cited work

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Observation f94e9d99-298f-4f66-97f5-9c22a12d7ec2 · outbound

This paper cites Temporal-aware siamese tracker: Integrate temporal context for 3d object tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Temporal-aware siamese tracker: Integrate temporal context for 3d object tracking

Reference 18

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Source-reported events for the cited work

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Observation 36a211f7-866d-43b0-85d4-646556000e84 · outbound

This paper cites Citetracker: Correlating image and text for visual tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Citetracker: Correlating image and text for visual tracking

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 9b6ec41a-22f1-4b9b-b667-78cf1dab68ea · outbound

This paper cites Seq- track3d: Exploring sequence information for robust 3d point cloud tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Seq- track3d: Exploring sequence information for robust 3d point cloud tracking

Reference 20

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Source-reported events for the cited work

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Observation d0406810-c8dc-424f-8795-fe3e620284cb · outbound

This paper cites Visual instruction tuning.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Visual instruction tuning

Reference 21

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Source-reported events for the cited work

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Observation 12350588-e199-4356-9309-e42a53a4c9e1 · outbound

This paper cites M3sot: multi-frame, multi- field, multi-space 3d single object tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking M3sot: multi-frame, multi- field, multi-space 3d single object tracking

Reference 22

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 90ed459f-1d6e-49ec-a23a-e88570b0ed02 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 23

Resolution
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Source-reported events for the cited work

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Observation d67b48f0-65ce-40e8-8c70-10f08fe693b0 · outbound

This paper cites V oxeltrack: Exploring multi-level voxel representation for 3d point cloud object tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking V oxeltrack: Exploring multi-level voxel representation for 3d point cloud object tracking

Reference 24

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Source-reported events for the cited work

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Observation 1f539faa-cc48-40ed-b7aa-49314e93a6f7 · outbound

This paper cites Modeling con- tinuous motion for 3d point cloud object tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Modeling con- tinuous motion for 3d point cloud object tracking

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation de807e23-8206-48cb-adc2-4f85127e10e1 · outbound

This paper cites Exploring point-bev fusion for 3d point cloud ob- ject tracking with transformer.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Exploring point-bev fusion for 3d point cloud ob- ject tracking with transformer

Reference 26

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f7a3a29b-956d-4a53-a8be-b245e6a49e6d · outbound

This paper cites Synchronize feature extracting and matching: A single branch framework for 3d object tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Synchronize feature extracting and matching: A single branch framework for 3d object tracking

Reference 27

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e2bfbe87-1483-4e8b-a8a4-3a1d38793817 · outbound

This paper cites Osp2b: One-stage point-to-box net- work for 3d siamese tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Osp2b: One-stage point-to-box net- work for 3d siamese tracking

Reference 28

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 552b2d3f-4f57-4627-ad07-19bf220ea85c · outbound

This paper cites Glt-t: Global-local transformer voting for 3d single object tracking in point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Glt-t: Global-local transformer voting for 3d single object tracking in point clouds

Reference 29

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e27c513a-f0f9-4735-a037-7ab31afa0fd4 · outbound

This paper cites Towards Category Unification of 3D Single Object Tracking on Point Clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Towards Category Unification of 3D Single Object Tracking on Point Clouds

Reference 30

Resolution
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local_arxiv, observed 2026-08-06T13:56:34.015675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation d225956c-ec66-4b29-80ad-f59b7ecaf067 · outbound

This paper cites P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking P2P: Part-to-Part Motion Cues Guide a Strong Tracking Framework for LiDAR Point Clouds

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:56:33.868900Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 89db613e-c5bb-4ec1-87f9-4bc15f29869c · outbound

This paper cites St-adapter: Parameter-efficient image-to-video transfer learning.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking St-adapter: Parameter-efficient image-to-video transfer learning

Reference 32

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e79f93d1-338b-4da0-84ad-1d9c78e7b258 · outbound

This paper cites Masked autoencoders for point cloud self-supervised learning.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Masked autoencoders for point cloud self-supervised learning

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:38.109964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.727798Z digest=sha256:627c0fac84b14d975d50a4bcd970a6e66a20c89fab3e8d5d7403b55f29828f1d

Observation c32d41ca-d0a6-4b3e-a198-cf609fb0a5ea · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-06T13:56:31.740431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:56:31.740431Z digest=sha256:73adfd422249dbfc62560b7d9dacaf9e4fc9dbab6b16f2ef38273074f8159f05

Observation e3ba968a-7607-4933-8b7b-ee6acce2c0b5 · outbound

This paper cites Deep hough voting for 3d object detection in point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Deep hough voting for 3d object detection in point clouds

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.935576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.751419Z digest=sha256:19d80e929509a67cbb451dc570182cf7b9555d2b398466c58bc8ee7d89776155

Observation 1513e9d5-c94a-4d49-8c9d-e0da35ce6f4b · outbound

This paper cites P2b: Point-to-box network for 3d object tracking in point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking P2b: Point-to-box network for 3d object tracking in point clouds

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.775506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.761508Z digest=sha256:e3e22621895d371f707f35e9b239e31528a0507475fc296b9dc4b947108e0319

Observation 19bbe229-cff3-45f3-a1a6-6d47c423ff53 · outbound

This paper cites Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.614844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.773969Z digest=sha256:32e6552566233b1cb6f707166f7b50a9c8028f7e5df1c5089bce2ac9f39bce60

Observation 93a8af8b-e6e8-4950-820a-417930847b90 · outbound

This paper cites Learning transferable visual models from natural language supervision.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Learning transferable visual models from natural language supervision

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.460460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.784969Z digest=sha256:f0b9f5b56ac68070b32224cb1518844a7627f659dabdac539d623a494c02c4dc

Observation d11ae026-8a21-4e77-be75-721474429847 · outbound

This paper cites Ptt: Point-track-transformer module for 3d single object tracking in point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Ptt: Point-track-transformer module for 3d single object tracking in point clouds

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.288217Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.794476Z digest=sha256:b539d5ffaa7e124aadc01b3ae41246b364c173685eb96e1b6cccd4b596c7f1a4

Observation 930e8e08-b6e3-4eb1-bcfc-f071fcb46009 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-06T13:56:31.813760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:56:31.813760Z digest=sha256:41d33040c83eb13d91a527440042d13ea998796cba85ca929852b9e903392f7f

Observation 21c691a3-112e-43a2-a23c-3ec029f46a0e · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Scalability in perception for autonomous driving: Waymo open dataset

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-06T13:56:31.828206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:56:31.828206Z digest=sha256:b4249797a302d3481a102322c2b65dd2d9b49e12021a4e3ca227c79430f32cef

Observation b28d8a60-3066-456d-ad60-463fb3446b80 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking LLaMA: Open and Efficient Foundation Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-06T13:56:31.843748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:56:31.843748Z digest=sha256:45f414e2d1d92c1372786694a5d8f0cd3cb3c1ff2c439a7c5673ab9ab9e92638

Observation 00553f43-b2e5-41c3-a992-12797b607d73 · outbound

This paper cites Correlation pyramid network for 3d single ob- ject tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Correlation pyramid network for 3d single ob- ject tracking

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.173335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.859123Z digest=sha256:14dff37f2c7c712f54ba8ed8b971e62e528fe53fc5324567088659dd65d2a470

Observation 819b2707-a359-4471-9361-5141f5cba1f1 · outbound

This paper cites Actionclip: Adapting language-image pretrained models for video action recognition.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Actionclip: Adapting language-image pretrained models for video action recognition

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:37.012684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.873731Z digest=sha256:10797ae595d607c5dc1e6439a5043ae992923ba059c7d94959221995ad609d64

Observation e9d39a8f-928c-4949-917a-ec213928a1a2 · outbound

This paper cites M2-clip: A multimodal, multi-task adapting framework for video action recognition.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking M2-clip: A multimodal, multi-task adapting framework for video action recognition

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.836936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.890734Z digest=sha256:612cea9c36cea3d0a8b55bc00b7a1f8284f2a164fc24fe95b395adb022d785a6

Observation 16cbe66e-6d49-4a32-b5b5-dec72ab1979b · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Dynamic graph cnn for learning on point clouds

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.682972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.907844Z digest=sha256:8baf743184409e5326c755d7521f877b10f210bcf616aefb43415d01bb7b14b9

Observation 81781aed-a55b-4271-80af-10387108ba6e · outbound

This paper cites Vita-clip: Video and text adaptive clip via multimodal prompting.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Vita-clip: Video and text adaptive clip via multimodal prompting

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.510522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:31.977714Z digest=sha256:79dcedcf6b10f00c6fad498ad0ac914c3f31aee5784b5c0d203e319c49d76524

Observation 2df80ed3-345d-4152-b82a-42d7a4992cba · outbound

This paper cites Boosting 3d single object tracking with 2d matching distilla- tion and 3d pre-training.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Boosting 3d single object tracking with 2d matching distilla- tion and 3d pre-training

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.370120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.049304Z digest=sha256:7c9a3ebff34a9356283293d7d33222eccdca5ec99d991e080b5fd3aaf5a37eb1

Observation d51db03b-1032-48fe-b8c2-1190e1185fe8 · outbound

This paper cites Pointcontrast: Unsupervised pre- training for 3d point cloud understanding.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.266139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.084438Z digest=sha256:4d30927e6bc414e36136f1b83b81e42a881fed7fdffd5e5a54837de2bb73edcf

Observation e58d9aa5-d45b-4c0e-91e9-f2282ed97d35 · outbound

This paper cites Cxtrack: Improving 3d point cloud tracking with contextual information.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Cxtrack: Improving 3d point cloud tracking with contextual information

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:36.079042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.199003Z digest=sha256:097345a19d07d4fa38bd18723fa1bd17f93246a78959c6fd2aa2d177f618ea7e

Observation ee3de25b-7e3c-4261-84c1-2651117b93a9 · outbound

This paper cites Mbptrack: Improving 3d point cloud tracking with memory networks and box priors.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Mbptrack: Improving 3d point cloud tracking with memory networks and box priors

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.935765Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.278869Z digest=sha256:875c1907f275c07754af1e9fbc847058c74aad0bec55be090dd95562ee080ceb

Observation c35e99c3-6d55-4f64-a017-b5183b17642c · outbound

This paper cites SiamMo: Siamese Motion-Centric 3D Object Tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking SiamMo: Siamese Motion-Centric 3D Object Tracking

Reference 52

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:56:33.679201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.346215Z digest=sha256:95970d0cb8ff1d86edb884e0087a4c6053abf26b5307d54c2781fe5b8116b465

Observation 835ff202-a65a-4e32-82bb-e75c9f444942 · outbound

This paper cites Joint feature learning and relation modeling for tracking: A one-stream framework.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Joint feature learning and relation modeling for tracking: A one-stream framework

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.779583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.444505Z digest=sha256:e173017bfd4ca5db6ceeb5f593b5dbcf2353d584d548c14cb514ab2b7ab31d85

Observation 266cac8f-0521-4875-9c53-ceb6067d1af5 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.623718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.580055Z digest=sha256:929cd6f0415a9f410c3530b640d5ced6759630c1652885fcc302c6adcdd4fbe8

Observation be2fca86-63dd-4775-b02f-7aa809f34786 · outbound

This paper cites Instance-aware dynamic prompt tuning for pre-trained point cloud models.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Instance-aware dynamic prompt tuning for pre-trained point cloud models

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.468482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.656763Z digest=sha256:2cbd034fe83072ba986e21cfa2c9d13b7f6be358ab35b4496014732c744d037a

Observation 2fbea7e5-6dc8-49f8-90ec-199bf5949909 · outbound

This paper cites Robust 3d tracking with quality-aware shape completion.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Robust 3d tracking with quality-aware shape completion

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.321656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.739870Z digest=sha256:1eb296164d51fc3d228f68d2a74587be503aeb8b19780a5eb9a386bd886d2b54

Observation 10e830ff-1b4f-4dfa-8abf-d5aa7db31531 · outbound

This paper cites Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-06T13:56:32.818393Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:56:32.818393Z digest=sha256:8f39ced84de3557100f7bddb119b2993783e315a558f866d10c8a9140cbbb7df

Observation a5cf4468-0c26-4a72-8d36-23cb2fa10f13 · outbound

This paper cites Pointclip: Point cloud understanding by clip.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Pointclip: Point cloud understanding by clip

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.173477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:32.935143Z digest=sha256:04de5541d0fd162251cba06c8a037817074d214209ac0190b7f9e9904cb9336f

Observation 2dbcc65a-4348-4b31-9828-843e3890f9e9 · outbound

This paper cites Box-aware feature en- hancement for single object tracking on point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Box-aware feature en- hancement for single object tracking on point clouds

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:35.040892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.002593Z digest=sha256:94bce57555df99901188652c01101cc34b118f965ea00f335b2726ebaeec9603

Observation 28e17a64-3c71-4cd8-ba54-03ced89d2103 · outbound

This paper cites Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Beyond 3d siamese tracking: A motion-centric paradigm for 3d single object tracking in point clouds

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:34.890149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.085273Z digest=sha256:9d68319a3633d2e06abfb5f7b56ade5af77e3d798c1b9d15a777a128f3d3b7d6

Observation f45a1654-3266-41bf-8ea5-f030eb1e28c3 · outbound

This paper cites Odtrack: Online dense temporal token learning for visual tracking.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Odtrack: Online dense temporal token learning for visual tracking

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:34.778005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.185662Z digest=sha256:c772bdfc451bf1233196c899e6f78be47d01310bdff7a3d9624dbcd14a2aa70a

Observation 857194c5-f124-48ae-b1d1-5de614899427 · outbound

This paper cites Pttr: Relational 3d point cloud object tracking with transformer.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Pttr: Relational 3d point cloud object tracking with transformer

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:34.604003Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.246022Z digest=sha256:432e3552099eb4dfeba4a6459f2fa2caffdc2e6822d3311684fa5888682393be

Observation ee6c56b4-16af-4f85-bbbc-db75cdb483ee · outbound

This paper cites Learning to prompt for vision-language models.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Learning to prompt for vision-language models

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:34.397250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.282731Z digest=sha256:1b7a03171a7fedda183544f3b3a5b4da86ea4ee466f44d90ad0c70f1c9dfd66d

Observation 94b194a9-7157-4c0f-832c-2734366993de · outbound

This paper cites Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis.

TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:34.190919Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T13:56:33.382609Z digest=sha256:93792567341b5f05c290d56f558659f642290861d367078d58295bdc3d746672

Pith citing papers

No inbound Pith citation observations are available.