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Paper Citation Record · LEDGER

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

As of 10 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 2026-08-09T06:31:02.800959+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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Source-reported events for the cited work

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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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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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Source-reported events for the cited work

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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

Resolution
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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.

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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 2026-08-09T06:31:02.800959+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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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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
verified fuzzy
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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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

Resolution
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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.

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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
verified fuzzy
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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

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

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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.

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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

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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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

Resolution
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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

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:39.549018Z

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.

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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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:39.311946Z

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.

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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 2026-08-09T06:31:02.800959+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
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+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
raw_fallback, observed 2026-08-06T13:56:38.612626Z

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.

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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
verified fuzzy
raw_fallback, observed 2026-08-06T13:56:38.425473Z

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.

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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
verified exact
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 2026-08-09T06:31:02.800959+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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.704636Z digest=sha256:7d205c8543767890e400833e2be548b1a29da6ad044e18c12042fbf1ad3efd51

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
raw_fallback, observed 2026-08-06T13:56:38.258863Z

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=2026-08-06T13:56:31.716330Z digest=sha256:71bbb3bf10c47cf2514744b2b426aa309658336b4c6a4da460ac91845bc72772

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.727798Z digest=sha256:86321597292f1006022ffde213824d1f46a45dc0b0a56e4185bc415441890619

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=sha256:4fd0414e71bf868a3d23d826ac8242214cf430a8e6e3013241890686f5465fe6

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.751419Z digest=sha256:49f62553deb2a1fad35f3994b4be3b993babb1acbe3c5685d0cf7cb5ae3ee09b

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.761508Z digest=sha256:ec5f87abc9b90b7586d8306be0817dd67d88979e936a56c9481f6c6d9021f418

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.773969Z digest=sha256:61b3b27f32ceef3a4ee399cab46480aadb0bd16dd4e5d6e5399ccca11aae6e25

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.784969Z digest=sha256:d91e9efed9391c8e9f9799c2a173cb141349c19791fb50cca7413c8448e3e9a2

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.794476Z digest=sha256:8dfc4a3177c03042f1ba7a71093fbb3743b25e8d26e8ed2bcbb1ad89ff092d66

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:f094a03451e7dea57b3a56463156112a5a039930acc99a60de9a4d5fd1576ae2

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:a377dc6935c93d3da4e94729d15b67e9ac72e00092796181e771392e3d8ffc83

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=sha256:62f7f0d14838e34c9a421f12f112c760fda5342d4d11d000b143df685f3e9566

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.859123Z digest=sha256:31a37f3b11311d6f2ebffc58fde36e77fed7774b87c67b68da859e083b375e9a

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.873731Z digest=sha256:74b1096016495c0da2063e7b9fb5662d45df1c37070a5b951e541c0b7690b301

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.890734Z digest=sha256:89c8011312600fa0ad8eb63ec1b876eca52a8a508f932e5ddadd65295e217b29

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.907844Z digest=sha256:5dd8969f5bcfbf8c420ae9ec6912826d4df0438890ede2dcbb64bc53a2f7cda1

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:31.977714Z digest=sha256:87757ee1ab3157223cd7f35f999bd636ea50c523ad0ae38679ba6ac5ca34a4a5

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.049304Z digest=sha256:1291ab97cc9d338f3b84248c8bec45c26c8ea83f9d5b2ed9640653702752a8bf

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.084438Z digest=sha256:7db9b102d34e966f02732a0c695a17595295f5a45e3c49466e9fe2757631ff9f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.199003Z digest=sha256:2331c54ca449eb1b68b8afcb6a67db74e7c7a5b7801430155ffdbc070d6d465f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.278869Z digest=sha256:057824ed6fd34027a9a790b93da8bc333cb1f8a2259d615c57d549c9a97d5786

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.346215Z digest=sha256:cbd6a8c0010ce7cdc7c10baf91088cc731e8fa354f0c96ae84d16e87f46e206f

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.444505Z digest=sha256:95bc0d820a9781750db9ff4642e292b65acb21b589ea93ec5dc7002621e4ce85

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.580055Z digest=sha256:1e711ff33b17fa453084f9e6085b0aba2474155259996ea153b1c591f204b246

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.656763Z digest=sha256:31df8e75dc849b99eb3366d1310639e6b968f3e3c97d1696662350bc62b97c4d

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.739870Z digest=sha256:88a612a93bd732b48eae72670dd79398bba6b2b444a8626b595aac370fff149b

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=sha256:854b3ee3c5be40d7ecc02ef90e148a5a25c6156bbd2232a1126d47238843e0f0

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:32.935143Z digest=sha256:a547700ee332846e4652d6d16f8bc1361dda50c6f23224e4e555a341f8e5c021

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.002593Z digest=sha256:79486ca353424cbecdaeee07587c0d262e71d0119f89f6aa27c1e81f5713a9ed

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.085273Z digest=sha256:20a7e44b6a600797d00d0ba45516c3001ccc04778a65c0655124175c087ee292

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.185662Z digest=sha256:2a82d518cb726083c6cc8077703a2253726a2007c49b049d6cf0116ab5d71dc7

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.246022Z digest=sha256:3e4ca5e4ffe14de2c9814da4f6ce1949b2053659881c5a068403b47927a62b02

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.282731Z digest=sha256:21e5f617e6d92d774a75b7b77ba23bf643dd99d6801752e21c37c0dd82d5a6d6

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 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T13:56:33.382609Z digest=sha256:eb956b6038cf91e2ccd46cf509630778d185d3ff66ed07bd7e8fe3de482dc665

Pith citing papers

No inbound Pith citation observations are available.