Pith. sign in

Paper Citation Record · LEDGER

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 2 inbound Pith citation observations for arXiv:2505.04119.

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

pith.paper-citation-record.v1
2505.04119 v3

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:40:48.858506Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:08:17.393721Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:08:17.775329Z

Reference resolution

42 of 42 outbound references displayed

  • verified exact0
  • verified fuzzy19
  • unresolved23
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e37015b-d16d-44e2-b1ee-aa2dc53b8db6 · outbound

This paper cites Pointgpt: Auto-regressively generative pre-training from point clouds.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Pointgpt: Auto-regressively generative pre-training from point clouds

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.329636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.702260Z digest=sha256:a49e0d2e47d1409ae4a0bc771b22f4b209e68f893f8fb7b3401b6e2507fd5e39

Observation 628b75e0-1dcd-45a4-a111-c6a41033a50d · outbound

This paper cites Adaptformer: Adapting vision transformers for scalable visual recognition.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.707128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.707128Z digest=sha256:b0a39d5dd03248b94072908c0a992efb259b746c59009ae93467b9d2171c8e9a

Observation 57554028-4f40-4647-a38a-656e198f9bb5 · outbound

This paper cites Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.710891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.710891Z digest=sha256:590cef8143526d2fb09c2636bd5a8a41ddd5a7d05d7a279a4a4348b445aa4768

Observation 4efbd89c-28f1-4eca-8bbf-950b6c49a51d · outbound

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

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model An image is worth 16x16 words: Transformers for image recognition at scale

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.715743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.715743Z digest=sha256:ad88a60955982d0590d132aec574d9a57595aff6c43d19ec1924e7fa86ac884d

Observation 7e1b9d53-652a-4023-b76e-ef01c09ab5c3 · outbound

This paper cites Towards a Unified View of Parameter-Efficient Transfer Learning.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Towards a Unified View of Parameter-Efficient Transfer Learning

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.719516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.719516Z digest=sha256:31fe688abbe0ba9ab48425b625ddd295ba90be46d2f214565566545b098b3b8a

Observation ce1e215b-998e-4c68-bb11-1fd0f5281eea · outbound

This paper cites Parameter-efficient transfer learning for nlp.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Parameter-efficient transfer learning for nlp

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.723481Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.723481Z digest=sha256:4f00f6b55abf747cfa94589de88e4487c34434650e8ac3cec202d706598720de

Observation 7ae309ae-3877-425a-8d07-9386c1e38112 · outbound

This paper cites W., Ouyang, W., and Zuo, W.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model W., Ouyang, W., and Zuo, W

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.298438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.727365Z digest=sha256:e5e055b22f7d42e9da13188ca11e015a07d515e098199e5742648514e302ead4

Observation 9e5e375d-d717-448a-a9a5-75bf8d9f8cc1 · outbound

This paper cites Visual prompt tuning.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Visual prompt tuning

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.731027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.731027Z digest=sha256:2b9ccdc69acb7903fdd58dee24682f997f219bcb1aba1fe9d8560f1ddd68b039

Observation 0d11663d-d0d6-4572-a7e4-949d717d6bd3 · outbound

This paper cites and Deng, Z.-H.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model and Deng, Z.-H

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.281166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.734614Z digest=sha256:8da1944bbc7f5cb3e62c66e35e610d68a674e68d65454d45ef6ef03702a3ef98

Observation 779dac58-8c6f-435b-aab0-9a22371491e9 · outbound

This paper cites Compacter: Efficient low-rank hypercomplex adapter layers.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Compacter: Efficient low-rank hypercomplex adapter layers

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.738745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.738745Z digest=sha256:d667cc050c52e24ec98f49f4d862f29d306c51c9f14f493c917c1928f426a8ae

Observation 52f37678-8331-423a-9978-fe4b7e342c87 · outbound

This paper cites The Power of Scale for Parameter-Efficient Prompt Tuning.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model The Power of Scale for Parameter-Efficient Prompt Tuning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.742645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.742645Z digest=sha256:622af107b6553deaeb3a54a7fa45b9d5153c1aa4cdc662778332640f68dfd608

Observation 3d19e0bb-ad2b-4aaa-b1b1-cadca7b2fcba · outbound

This paper cites and Zhou, J.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model and Zhou, J

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.263514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.746762Z digest=sha256:b3686449eb69b3b2b35e6b5efa817bfbbfd9a0fb8f2880bfc0ea5db9ceaa582f

Observation c03f9d14-afc7-4c3c-8f01-47ff23d9128a · outbound

This paper cites Prefix-Tuning: Optimizing Continuous Prompts for Generation.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Prefix-Tuning: Optimizing Continuous Prompts for Generation

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.750302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.750302Z digest=sha256:c10146de75e46038f8c3f4a59f66b5b88f2a492d28faa0f15fa456567bc4c3e1

Observation 0cd4ebe4-e610-410b-9ffb-6acb764847ce · outbound

This paper cites Scaling & shifting your features: A new baseline for efficient model tuning.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Scaling & shifting your features: A new baseline for efficient model tuning

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.253262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.754101Z digest=sha256:fb044045055d0dcf000c4f42b4b62c2e42b14d8193105a5721908cc1038a80b8

Observation 938b23b9-e893-4a98-b396-505524d2ac28 · outbound

This paper cites an unresolved cited work.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Unresolved cited work

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.757656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.757656Z digest=sha256:d8050ad5b26f77d22d9f0feeb2c5ef81a8dca70954f2174b501527a24a745f1e

Observation e7ae335e-5943-4e27-a4d0-c310db99609a · outbound

This paper cites Relation-shape convolutional neural network for point cloud analysis.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Relation-shape convolutional neural network for point cloud analysis

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.235594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.761143Z digest=sha256:509f2411dae3506605741a208a4a46659cfc541ba5d25c70e1840a5c096f7c65

Observation bd815903-6b34-4d46-89d0-8dff0cf21dae · outbound

This paper cites Insvp: Efficient instance visual prompting from image itself.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Insvp: Efficient instance visual prompting from image itself

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.224584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.764379Z digest=sha256:b9e795773412266e8fce7d7ab627232f463953a468bf89540b9829cc81affde8

Observation 6a0ccebc-ab22-49e5-9979-6b77c9c8a42f · outbound

This paper cites and Hutter, F.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model and Hutter, F

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.767717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.767717Z digest=sha256:cdc51d1710fd3c7205189c9ad1ba9b2291372cbd3ae6a6175d944ab716f386d4

Observation d09e6803-a07e-4867-a59d-a4b1361315af · outbound

This paper cites and Hutter, F.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model and Hutter, F

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.771649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.771649Z digest=sha256:c242ed042f9bb3d5fde63625c9b50c2b2b934293fb31dcb04e2b99787cf7d0bd

Observation 5fd0c85c-c256-41f3-8ec0-d19ea288b760 · outbound

This paper cites Scaffold-gs: Structured 3d gaussians for view-adaptive rendering.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.200574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.776833Z digest=sha256:f90a9156581b68529398382dc446342aee4e7af7c475e4b9e16725e596006ddb

Observation 88fdcfc6-a614-405f-9703-cb346b772c42 · outbound

This paper cites E., Liu, W., Tian, Y., and Yuan, L.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model E., Liu, W., Tian, Y., and Yuan, L

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.189409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.780253Z digest=sha256:961e1216f0b072503d0376e3b9465b5722586c4dbbad71a4a9e4b8943ff756b3

Observation 6b4efa46-27f4-4a26-add3-416ea6ff7691 · outbound

This paper cites V., Le Nguyen, M., Nguyen, Y.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model V., Le Nguyen, M., Nguyen, Y

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.178959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.783694Z digest=sha256:31cbdb3adcc329faf97b6833945b03680ef1e186d23cfe7c6442eee57e7c3234

Observation 14cdd5a7-462d-472b-ad27-1b62c417a652 · outbound

This paper cites R., Su, H., Mo, K., and Guibas, L.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model R., Su, H., Mo, K., and Guibas, L

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.787351Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.787351Z digest=sha256:54d5df447280584dbd174123478de1564e086ef74c74e6c98850cad0354de5ae

Observation b9eccb94-cc95-497a-8606-db8218b798d3 · outbound

This paper cites R., Yi, L., Su, H., and Guibas, L.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model R., Yi, L., Su, H., and Guibas, L

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.790985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.790985Z digest=sha256:4820d48dd269272d96e66cd34811959db5a7b912eff100980316d7cfdc5f43a7

Observation 9f610ae6-4188-4b87-ae52-246845e44aef · outbound

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

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.155616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.794585Z digest=sha256:61bf2dfbefe0218d39e6e16b8a9cff38489756e3eef074f582aad263374d50bc

Observation cda32896-48e4-4cfe-8f12-35501683e3cb · outbound

This paper cites ShapeLLM: Universal 3D Object Understanding for Embodied Interaction.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model ShapeLLM: Universal 3D Object Understanding for Embodied Interaction

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.798058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.798058Z digest=sha256:9ea99cd84a3054242e6e815f379f93a417b49f7cc5512bb78377a4148a0edf74

Observation d5ef8232-278c-4d68-bd3c-22d98978718a · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Dropout: a simple way to prevent neural networks from overfitting

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.801982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.801982Z digest=sha256:92e01d029a5b6880ab84492310cac6a826d95b7a4e477eeef77f79f3af6fb5bf

Observation bd82b873-3407-4a3a-9acc-32ab6e9d2944 · outbound

This paper cites Point-peft: Parameter-efficient fine-tuning for 3d pre-trained models.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Point-peft: Parameter-efficient fine-tuning for 3d pre-trained models

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.137100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.805894Z digest=sha256:4936e830989e208c011cec6f819946d018c9a6aba363bc710593e476c369b29a

Observation d8283d97-aea8-42bf-8f2d-e8fa45ffa924 · outbound

This paper cites A., Pham, Q.-H., Hua, B.-S., Nguyen, T., and Yeung, S.-K.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model A., Pham, Q.-H., Hua, B.-S., Nguyen, T., and Yeung, S.-K

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.809382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.809382Z digest=sha256:5737a58a202a5cd3d2de09b00a3fde670b972b1c190d4e963542a501c4b07999

Observation 608c4c0f-1ca2-4e42-a0b1-e67225565129 · outbound

This paper cites and Hinton, G.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model and Hinton, G

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.812855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.812855Z digest=sha256:2aa61cbd417eacfef416345c0f3c7645bed78f1e2d29f3a7ad9b4cf46c9bf4f9

Observation fa12cc08-9765-4a37-9394-f3556f030833 · outbound

This paper cites an unresolved cited work.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Unresolved cited work

Reference 31

Resolution
unresolved
raw_fallback, observed 2026-08-15T23:40:49.112448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.816339Z digest=sha256:15fd0d9191a69567a25734c1febda19bd673a141b547bb50aaa261635f183b1c

Observation 0465abec-6dcd-4c83-ab30-44949c77ec76 · outbound

This paper cites 3d shapenets: A deep representation for volumetric shapes.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model 3d shapenets: A deep representation for volumetric shapes

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.820013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.820013Z digest=sha256:91ca2d12e2a1b39a2678ec2d6ab0cc090ab43b7a7229fd1c9c2bc90b24e4779b

Observation b297200f-d676-4fcd-8fa8-f647d471b189 · outbound

This paper cites Point-nerf: Point-based neural radiance fields.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Point-nerf: Point-based neural radiance fields

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.095339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.823752Z digest=sha256:4fa634cfd34fb06719f29f36c0a05568c44cc42f735894a7dfd8950ca9650871

Observation 9a0ae41a-bbfc-4721-8d4d-e6c4b6760a05 · outbound

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

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.827662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.827662Z digest=sha256:475d18bdf723dc3f9dfc13e5eaadc9a8f8b86b2343d62546d8b766aec1eefd03

Observation 4e4fa959-c8be-43cd-85b6-05b575e78ce8 · outbound

This paper cites B., Ravfogel, S., and Goldberg, Y.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model B., Ravfogel, S., and Goldberg, Y

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.831553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.831553Z digest=sha256:ca3b4f96a51a850e0666b7df393dfcb6308a1f35cd4c2783548223e904228aeb

Observation 438bb6fb-8dd7-4fc6-8259-09c6e9c15f14 · outbound

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

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Instance-aware dynamic prompt tuning for pre-trained point cloud models

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.077560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.835565Z digest=sha256:8fc5e9dac77cbf66fda9bcfe2c2f66e9d9fe50073bf7429b38b28ad8eb9be3e8

Observation 8cc639c3-225a-4146-8087-3acc758001db · outbound

This paper cites Towards compact 3d representations via point feature enhancement masked autoencoders.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Towards compact 3d representations via point feature enhancement masked autoencoders

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.064588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.839369Z digest=sha256:23c4be4d38fb5eadd03733bf96e89693325744e828f1356e0579b76823c7a2b4

Observation 2a855b34-5b7a-48e1-a9ca-8ac7c341800f · outbound

This paper cites Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.052867Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.843022Z digest=sha256:9511327da9b7ef262492cad95d3cbfe2c5a3a9f1c4728eb869f2d2e8e3bf5e14

Observation d3cb803d-59ef-4908-9c61-57dd92334ac2 · outbound

This paper cites Pnerfloc: Visual localization with point-based neural radiance fields.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Pnerfloc: Visual localization with point-based neural radiance fields

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.039411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.846820Z digest=sha256:55f23f3d2fe849a854f104930174b0eead4e33a260a012fbfb9bfa4fc9eaaaa8

Observation 60ae329c-6422-449e-a7d6-a7633e6a2ead · outbound

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

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.028200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.850712Z digest=sha256:c1d8d9763eb2035639fd209f49dd83982a41d676d3f9c20c16f467b1d750d13f

Observation 110af22e-1689-43a8-86cb-49d090802409 · outbound

This paper cites Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T23:40:49.016917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T23:40:48.854637Z digest=sha256:a7ced6d7e930e7381ebcf90d8c258aea994fdfc3a181e773364a66c4c489a4f5

Observation 464be225-3729-4b2f-98eb-9c60a9ad7b50 · outbound

This paper cites write newline.

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model write newline

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T23:40:48.858506Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T23:40:48.858506Z digest=sha256:b4022bb326b3de2320412c67d5d94d76d9ea58929865f6bf552b8cb784f5df0c

Pith citing papers

Observation 831da27f-0c57-425d-9633-4331edbe5310 · inbound

UPP: Unified Point-Level Prompting for Robust Point Cloud Analysis cites this paper.

UPP: Unified Point-Level Prompting for Robust Point Cloud Analysis GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-15T18:08:17.780462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-15T18:08:17.393721Z digest=sha256:af571873b1a99be02452c4bcdd10611cf0762d91ce42c81fa624996ec36a1f2f

Observation 7e675dd5-ff7e-426d-b9d4-9b69e9623cd4 · inbound

Fast 3D Foundation Model Initialized Gaussian Splatting cites this paper.

Fast 3D Foundation Model Initialized Gaussian Splatting GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

Reference 16

Resolution
unresolved
no resolver link, observed 2026-07-12T04:07:24.338434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T04:07:24.338434Z digest=sha256:133d438563567cdee812ed9ecd9c7f654fa64c9fc2a150714d63ff37ca168131