Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis | Paper Citation Record

Observation a2cb5c27-721f-463f-992a-8cdfacc62c42 · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis A simple framework for contrastive learning of visual representations,

Reference 1

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Observation 7b1672ce-0f18-495f-98e1-0736967a03e7 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis LoRA: Low-rank adaptation of large language models,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:39.109311Z

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Observation 41c57945-d6eb-481d-b062-d355bc27d74f · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Adaptformer: Adapting vision transformers for scalable visual recognition,

Reference 3

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Observation cbbe6324-2e0f-4130-a270-f75293a44c85 · outbound

This paper cites Visual tuning,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Visual tuning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:39.357021Z

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Observation 621d7e34-09d7-4990-8627-8d4b78814d19 · outbound

This paper cites Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pre- train, prompt, and predict: A systematic survey of prompting methods in natural language processing,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:39.455088Z

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Observation 19f5e3ad-cab6-4ccd-aba4-2ad3c13042cb · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:39.545485Z

Source-reported events for the cited work

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Observation 45c12cec-13f5-45cb-b937-d10a9e756b75 · outbound

This paper cites Gpt-3: Its nature, scope, limits, and consequences,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Gpt-3: Its nature, scope, limits, and consequences,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:39.632381Z

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Observation 18e93dad-f86e-4f0d-b109-b96cdd1fcec0 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:46:00.424306Z

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Observation 34eb7a63-741f-4be1-ae4d-0e8f715458eb · outbound

This paper cites Learning trans- ferable visual models from natural language supervision,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Learning trans- ferable visual models from natural language supervision,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:46:00.250810Z

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Observation 803eae8a-718b-4e07-a1b5-ef2b3b48ebe6 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis DINOv2: Learning robust visual features without supervision,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:46:00.105747Z

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Observation c38a1212-1e0f-48b7-baa5-b200ffecb96b · outbound

This paper cites Deep learning for 3d point clouds: A survey,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Deep learning for 3d point clouds: A survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:59.861091Z

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.

Observation 922886da-7cce-420b-bad9-9e3d2213aeb1 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point- bert: Pre-training 3d point cloud transformers with masked point modeling,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:59.701232Z

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

Observation 4f9fd6cd-a2fd-4220-84a1-36912f269d78 · outbound

This paper cites Unsuper- vised point cloud pre-training via occlusion completion,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Unsuper- vised point cloud pre-training via occlusion completion,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:59.533567Z

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.

Observation e89a7315-88b5-4b79-8f32-08ee5c600db4 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointgpt: Auto-regressively generative pre-training from point clouds,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:59.371167Z

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Observation 60aa2919-6c3b-43cd-a95d-3f51bad2b9af · outbound

This paper cites Any2point: Empowering any-modality large models for efficient 3d understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Any2point: Empowering any-modality large models for efficient 3d understanding,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:59.136296Z

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.

Observation 9b3e9107-ee16-4cdf-9294-e5370aecc100 · outbound

This paper cites P2P: tuning pre- trained image models for point cloud analysis with point-to-pixel prompting,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis P2P: tuning pre- trained image models for point cloud analysis with point-to-pixel prompting,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:58.935506Z

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.

Observation 30408cdd-1094-4df3-92e8-85d889d68528 · outbound

This paper cites Flattening-net: Deep regular 2d representation for 3d point cloud analysis,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Flattening-net: Deep regular 2d representation for 3d point cloud analysis,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:58.740459Z

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Observation e9f87724-026b-4e87-98ca-115f00578cb5 · outbound

This paper cites Point-to-pixel prompt- ing for point cloud analysis with pre-trained image models,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point-to-pixel prompt- ing for point cloud analysis with pre-trained image models,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:58.560704Z

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Observation 79ec9d2d-8c3b-4f68-99c1-0dfa82fd9f98 · outbound

This paper cites Pointllm: Empowering large language models to understand point clouds,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointllm: Empowering large language models to understand point clouds,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:58.342488Z

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Observation 8f6b45c6-8a2e-4fbd-a637-cca3ba5512ad · outbound

This paper cites Learning 3D representations from 2D pre-trained models via image-to-point masked autoencoders,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Learning 3D representations from 2D pre-trained models via image-to-point masked autoencoders,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:58.172486Z

Source-reported events for the cited work

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Observation 0f90043d-afe2-4882-bfe5-1d9e28bd9454 · outbound

This paper cites Openshape: Scaling up 3d shape representation to- wards open-world understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Openshape: Scaling up 3d shape representation to- wards open-world understanding,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.974482Z

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.

Observation 2b0c3267-ce60-403e-a475-2dcbe5f370ee · outbound

This paper cites Partdistill: 3d shape part segmentation by vision-language model distillation,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Partdistill: 3d shape part segmentation by vision-language model distillation,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.808340Z

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.

Observation c497b525-b653-4843-a792-af3def19cdd4 · outbound

This paper cites ULIP: Learning a unified representation of language, images, and point clouds for 3d un- derstanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis ULIP: Learning a unified representation of language, images, and point clouds for 3d un- derstanding,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.618540Z

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

Observation d93ff650-a5fa-4ed8-b90c-ee84f843a7c4 · outbound

This paper cites ULIP-2: Towards scalable multimodal pre-training for 3d understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis ULIP-2: Towards scalable multimodal pre-training for 3d understanding,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.435926Z

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.

Observation 42c4a677-1901-401d-aa38-0c378c322ca8 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.271091Z

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

Observation 887adba9-6725-4823-99d7-2b5bbbdffdf1 · outbound

This paper cites Deep sets,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Deep sets,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:57.093440Z

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.

Observation 66a646bd-385c-45c3-aacb-5f8bbcfc115b · outbound

This paper cites Adapt point- former: 3d point cloud analysis via adapting 2d visual transform- ers,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Adapt point- former: 3d point cloud analysis via adapting 2d visual transform- ers,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:56.928534Z

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.

Observation b18efe38-2b6c-4aff-b4c6-4f51bb710636 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:56.715941Z

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.

Observation 9a64e383-1046-4bb5-964b-3eff02ded156 · outbound

This paper cites Point-voxel cnn for efficient 3d deep learning,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point-voxel cnn for efficient 3d deep learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:56.576977Z

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.

Observation d5a6732b-9b4b-45e4-8bd0-38f9b360e64b · outbound

This paper cites Pv- rcnn: Point-voxel feature set abstraction for 3d object detection,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pv- rcnn: Point-voxel feature set abstraction for 3d object detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:56.415258Z

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.

Observation 78bb7e7c-b5f9-46c5-aca1-1f98ae777050 · outbound

This paper cites Surface representation for point clouds,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Surface representation for point clouds,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:56.272094Z

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.

Observation 779bc2e7-ef7b-481b-9407-69a17f8b6822 · outbound

This paper cites Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Bevdepth: Acquisition of reliable depth for multi-view 3d object detection,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:55.995208Z

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.

Observation 6ec6f592-1115-450c-9003-5280e0e8ad85 · outbound

This paper cites Pointnext: Revisiting pointnet++ with improved training and scaling strategies,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointnext: Revisiting pointnet++ with improved training and scaling strategies,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:55.780740Z

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.

Observation 2e15005f-1909-4619-8637-e467ab4b3cfc · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Dynamic graph cnn for learning on point clouds,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:55.635908Z

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.

Observation 58c5bc17-b71c-4876-9f44-fff1b19d609a · outbound

This paper cites Kpconv: Flexible and deformable convolution for point clouds,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Kpconv: Flexible and deformable convolution for point clouds,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:55.440715Z

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.

Observation 43a6986d-be8f-45d6-b355-605d2b1448c6 · outbound

This paper cites Attention is all you need,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Attention is all you need,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:55.211108Z

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.

Observation f06af01a-21d3-4138-9e9a-59b37e396b2d · outbound

This paper cites Point trans- former,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point trans- former,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:54.940446Z

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.

Observation 4a496182-ee03-4d52-bd10-51d1d785859e · outbound

This paper cites Pct: Point cloud transformer,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pct: Point cloud transformer,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:54.689353Z

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.

Observation bf5762ed-5215-4631-8d60-006f7478718c · outbound

This paper cites Pointmixer: Mlp-mixer for point cloud understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointmixer: Mlp-mixer for point cloud understanding,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:54.452210Z

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.

Observation 69ad56bd-1ed9-4920-b78c-9f1d5c4203a2 · outbound

This paper cites Point transformer v2: Grouped vector attention and partition-based pooling,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point transformer v2: Grouped vector attention and partition-based pooling,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:54.176088Z

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.

Observation df29ce9c-2e81-4976-bc3c-31e2055aff98 · outbound

This paper cites Condaformer: Disassembled transformer with local structure en- hancement for 3d point cloud understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Condaformer: Disassembled transformer with local structure en- hancement for 3d point cloud understanding,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:53.933317Z

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.

Observation 71971b36-efd5-4f4b-b3b7-533d094b4187 · outbound

This paper cites Mamba3d: Enhancing local features for 3d point cloud analysis via state space model,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Mamba3d: Enhancing local features for 3d point cloud analysis via state space model,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:53.675741Z

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.

Observation ab104677-d945-41ad-b560-af5480c95205 · outbound

This paper cites Point transformer v3: Simpler faster stronger,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point transformer v3: Simpler faster stronger,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:53.308370Z

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.

Observation bcf85586-ebfe-4eb7-96fa-9223827dbd1a · outbound

This paper cites Multimodal token fusion for vision transformers,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Multimodal token fusion for vision transformers,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:52.960913Z

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.

Observation 79a58513-4316-4db5-90b0-00bf7b3ace22 · outbound

This paper cites Pointofview: A multi-modal network for few-shot 3d point cloud classification fusing point and multi-view image features,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointofview: A multi-modal network for few-shot 3d point cloud classification fusing point and multi-view image features,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:52.597312Z

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.

Observation bb78ca07-929a-465a-8b09-201f4cd66725 · outbound

This paper cites Ashapeformer: Semantics-guided object-level active shape encoding for 3d object detection via transformers,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Ashapeformer: Semantics-guided object-level active shape encoding for 3d object detection via transformers,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:52.313297Z

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.

Observation f5bcd77f-bc55-4549-8636-1590182b5fe4 · outbound

This paper cites Point cloud pre-training with diffusion models,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point cloud pre-training with diffusion models,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:51.992799Z

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.

Observation e0f209b8-b3a8-49e7-b70f-542fabe4c3df · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point-peft: Parameter-efficient fine-tuning for 3d pre- trained models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:51.726467Z

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.

Observation 8b2d4cf8-2ca0-4661-bde7-d7a2780e12f2 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Masked autoencoders for point cloud self-supervised learning,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:51.507332Z

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.

Observation 3f3f11ac-1f01-4615-ba6e-06e3ed5f1d6d · outbound

This paper cites Point-m2ae: Multi-scale masked autoencoders for hi- erarchical point cloud pre-training,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Point-m2ae: Multi-scale masked autoencoders for hi- erarchical point cloud pre-training,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:51.329289Z

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.

Observation 4d04b60f-78b2-4910-952a-101a06d594bc · outbound

This paper cites PointCLIP: Point cloud understanding by clip,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis PointCLIP: Point cloud understanding by clip,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:51.143493Z

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.

Observation 84e3e6e1-df9d-4cab-90c7-6ae69884b17b · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.907806Z

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.

Observation 2666cd48-b857-4ee2-93c3-0e1632a03330 · outbound

This paper cites View-GCN: View-based graph convolu- tional network for 3D shape analysis,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis View-GCN: View-based graph convolu- tional network for 3D shape analysis,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.747050Z

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.

Observation 71afe1da-9ba0-47cf-ae7b-e9d3569d440e · outbound

This paper cites Predicting the perceptual quality of point cloud: A 3d-to-2d projection-based exploration,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Predicting the perceptual quality of point cloud: A 3d-to-2d projection-based exploration,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.572542Z

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.

Observation 52d79863-9741-497c-8c3d-d1db36959b97 · outbound

This paper cites Data efficient 3d learner via knowledge transferred from 2d model,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Data efficient 3d learner via knowledge transferred from 2d model,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.435598Z

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.

Observation 6b85a97e-5b59-4390-a52a-985ea3a9070b · outbound

This paper cites Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning?.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning?

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.232598Z

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.

Observation b46f3ef7-0f9d-4dfe-ba5d-c46e6602ad63 · outbound

This paper cites Visual prompt tuning,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Visual prompt tuning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:50.061427Z

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.

Observation 6ee9fd03-edcc-4057-8341-055d018a06b0 · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis An image is worth 16x16 words: Transformers for image recognition at scale,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.917899Z

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.

Observation 0efd57b8-14a1-4d17-b275-73c6064cd8dc · outbound

This paper cites Developing real-time streaming transformer transducer for speech recognition on large- scale dataset,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Developing real-time streaming transformer transducer for speech recognition on large- scale dataset,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.737245Z

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.

Observation acb14da5-bf8b-4c21-9b3d-7b7aabf65755 · outbound

This paper cites Layer Normalization.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Layer Normalization

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:43.618364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 890b2384-fc27-4b48-aeb6-d947a843a272 · outbound

This paper cites Deep residual learning for image recognition,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Deep residual learning for image recognition,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.492654Z

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.

Observation a4c4803b-3bad-4604-b8cd-3a6c15ae2264 · outbound

This paper cites Rethinking network design and local geometry in point cloud: A simple residual mlp framework,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Rethinking network design and local geometry in point cloud: A simple residual mlp framework,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.300637Z

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.

Observation 6469a373-5d85-4acd-908e-13d44d69117e · outbound

This paper cites Starting from non-parametric networks for 3d point cloud analysis,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Starting from non-parametric networks for 3d point cloud analysis,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.233165Z

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.

Observation aedcbb9e-5ae4-4007-b35d-8b18755488b7 · outbound

This paper cites Tokens-to-token vit: Training vision transformers from scratch on imagenet,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Tokens-to-token vit: Training vision transformers from scratch on imagenet,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:49.026680Z

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.

Observation a5d17cd1-77ae-4eec-b466-b453eb082bc1 · outbound

This paper cites Exploring Visual Prompts for Adapting Large-Scale Models.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Exploring Visual Prompts for Adapting Large-Scale Models

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:43.985537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c74a8ba-9c09-45c1-8119-4bab5ed08fc5 · outbound

This paper cites Read-only prompt optimization for vision-language few-shot learning,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Read-only prompt optimization for vision-language few-shot learning,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:48.819579Z

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.

Observation a688528c-c796-4961-8dc6-a14c767e877e · outbound

This paper cites LPT: Long-tailed prompt tuning for image classification,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis LPT: Long-tailed prompt tuning for image classification,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:48.623313Z

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.

Observation 9b86dbf7-139e-4dad-ac82-d207905b6e6b · outbound

This paper cites Long- tail learning with foundation model: Heavy fine-tuning hurts,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Long- tail learning with foundation model: Heavy fine-tuning hurts,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:48.428605Z

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.

Observation ce6ced25-f3f8-45c9-a70d-23a87933b1e1 · outbound

This paper cites Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Improving Visual Prompt Tuning by Gaussian Neighborhood Minimization for Long-Tailed Visual Recognition

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:45:45.442260Z

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.

Observation 9e3add14-cc7b-472d-9208-31fd7fb68d85 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Prefix-tuning: Optimizing continuous prompts for generation,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:48.304669Z

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.

Observation c2e53274-85f6-497f-90cf-2d2d7f7fc94e · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Swin transformer: Hierarchical vision transformer using shifted windows,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:48.128224Z

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.

Observation c8380f35-4c8e-40e5-ba3b-6645d18f845c · outbound

This paper cites Deep sets,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Deep sets,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.957497Z

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.

Observation 16f46559-47ad-4670-a4dc-398d2ba4ee39 · outbound

This paper cites Deep set prediction networks,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Deep set prediction networks,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.724965Z

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.

Observation e7cd978d-3154-49ce-a4d0-9d8957449935 · outbound

This paper cites Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.565969Z

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.

Observation 044b8698-daf3-43e6-8e0b-a75f619834cb · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Dy- namic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.411910Z

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.

Observation 7fde19c4-6616-4373-b25b-425a47f1168c · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.290149Z

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.

Observation 5d43e415-6b4a-4ae4-bd1c-df30a36301b9 · outbound

This paper cites Crosspoint: Self-supervised cross- modal contrastive learning for 3d point cloud understanding,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Crosspoint: Self-supervised cross- modal contrastive learning for 3d point cloud understanding,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:47.158058Z

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.

Observation a7e6f7f5-ebfe-4477-b3c5-631e15a55f65 · outbound

This paper cites Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data,

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.978806Z

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.

Observation aeabc708-7474-44ba-878c-1e0f864c89ae · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis 3D shapenets: A deep representation for volumetric shapes,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.776238Z

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.

Observation 4dfdfc78-4aaa-49b6-97d9-dd48e0dd483f · outbound

This paper cites A scalable active framework for region annotation in 3d shape collections,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis A scalable active framework for region annotation in 3d shape collections,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.618539Z

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.

Observation 0df23663-1e6d-4018-825d-78063f17a5d4 · outbound

This paper cites Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Paconv: Position adaptive convolution with dynamic kernel assembling on point clouds,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.449368Z

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.

Observation a1091b29-9bac-44f2-96ee-55b5c2f7a05f · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Imagenet large scale visual recognition challenge,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.299191Z

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.

Observation 7b73e3a2-813a-4061-8121-5ab2b9024664 · outbound

This paper cites Imagebind: One embedding space to bind them all,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Imagebind: One embedding space to bind them all,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:46.084197Z

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.

Observation 845fe0af-d660-4429-925c-6f2a507dcd5c · outbound

This paper cites Training data-efficient image transformers & distilla- tion through attention,.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis Training data-efficient image transformers & distilla- tion through attention,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:45.887193Z

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.

Observation 6a69e434-4441-4058-a7a7-76a7eacda4ed · outbound

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

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-05T13:45:45.246814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10368bf5-a39c-41d7-b59b-71846582f531 · outbound

This paper cites His current re- search directions are computer vision and 3D point cloud analysis.

Adaptive Point-Prompt Tuning: Fine-Tuning Heterogeneous Foundation Models for 3D Point Cloud Analysis His current re- search directions are computer vision and 3D point cloud analysis

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:45:45.626515Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers