Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning | Paper Citation Record

Observation 2505c0ab-56a1-44a9-9cf8-66e14b75107a · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding

Reference 1

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This paper cites Se- mantickitti: A dataset for semantic scene understanding of lidar sequences.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Se- mantickitti: A dataset for semantic scene understanding of lidar sequences

Reference 2

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This paper cites Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets

Reference 3

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This paper cites Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Lan- guage models are few-shot learners.Advances in neural in- formation processing systems, 33:1877–1901, 2020

Reference 4

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This paper cites Emerg- ing properties in self-supervised vision transformers.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Emerg- ing properties in self-supervised vision transformers

Reference 5

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Observation 4c73ecaa-2eac-4521-b79b-e84cc140257e · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning ShapeNet: An Information-Rich 3D Model Repository

Reference 6

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This paper cites Pimae: Point cloud and image interactive masked autoencoders for 3d object detection.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pimae: Point cloud and image interactive masked autoencoders for 3d object detection

Reference 7

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Observation aff5a1ba-d889-4958-b229-96c4d307af1e · outbound

This paper cites Videocrafter1: Open diffusion models for high-quality video generation, 2023.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Videocrafter1: Open diffusion models for high-quality video generation, 2023

Reference 8

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Observation e9e74191-8316-4955-982b-59e0572697f8 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning A simple framework for contrastive learning of visual representations

Reference 9

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Observation 1f24c81b-2196-4607-9c39-e7ef2179976e · outbound

This paper cites Pointmixup: Augmentation for point clouds.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointmixup: Augmentation for point clouds

Reference 10

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Observation 9df3e67b-e750-44ee-91dd-60ba6ca0c5a2 · outbound

This paper cites Text-to-image diffusion mod- els are zero shot classifiers.Advances in Neural Information Processing Systems, 36, 2024.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Text-to-image diffusion mod- els are zero shot classifiers.Advances in Neural Information Processing Systems, 36, 2024

Reference 11

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Observation 4352d4c7-4cf5-48ca-9866-97c57396780d · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 12

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Observation aefa933c-376a-42c6-8969-a55e399c4e06 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 13

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This paper cites an unresolved cited work.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Unresolved cited work

Reference 14

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Observation 42aa2748-5f36-4325-820d-d5dea85c746e · outbound

This paper cites Revisiting point cloud shape classification with a simple and effective baseline.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Revisiting point cloud shape classification with a simple and effective baseline

Reference 15

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Observation 6d07a70c-38c8-44bb-86a6-60fd9b51760e · outbound

This paper cites 3d semantic segmentation with submani- fold sparse convolutional networks.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning 3d semantic segmentation with submani- fold sparse convolutional networks

Reference 16

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Observation a7405aea-e35e-4194-8b82-71e9f41b4522 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284, 2020

Reference 17

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Observation 47b51c72-e53a-4ee8-8b29-eb0af7ace969 · outbound

This paper cites AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning

Reference 18

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Observation 3b893064-dba0-4fe3-9606-5cc310dcbf84 · outbound

This paper cites Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training

Reference 19

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Observation 12cb7dfd-7395-4c00-b674-cfd2161e5da0 · outbound

This paper cites Mvtn: Multi-view transformation network for 3d shape recognition.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Mvtn: Multi-view transformation network for 3d shape recognition

Reference 20

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Observation f27cb8ec-7a16-4363-9974-a8a2d859a43d · outbound

This paper cites Masked autoencoders are scalable vision learners.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Masked autoencoders are scalable vision learners

Reference 21

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Observation 70167a6a-f384-47dc-bb13-e3439fa70495 · outbound

This paper cites Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Denoising dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851, 2020

Reference 22

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Observation 4ec609ef-8241-4a18-8792-e716dab9f03c · outbound

This paper cites Regu- larization strategy for point cloud via rigidly mixed sample.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Regu- larization strategy for point cloud via rigidly mixed sample

Reference 23

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Observation 45094009-f6c7-4fca-be21-9c3db0e4dd68 · outbound

This paper cites Your diffusion model is secretly a zero-shot classifier.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Your diffusion model is secretly a zero-shot classifier

Reference 24

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Observation 2ae84e8a-8dba-4962-a28b-5eca27a83494 · outbound

This paper cites Pointcnn: Convolution on x-transformed points.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointcnn: Convolution on x-transformed points

Reference 25

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Observation cb035155-e7b6-4715-b182-30b30b957f83 · outbound

This paper cites Masked discrim- ination for self-supervised learning on point clouds.Pro- ceedings of the European Conference on Computer Vision (ECCV), 2022.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Masked discrim- ination for self-supervised learning on point clouds.Pro- ceedings of the European Conference on Computer Vision (ECCV), 2022

Reference 26

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Observation 9b25c965-8b19-437e-8fff-24aeda182ac6 · outbound

This paper cites Zero-1-to-3: Zero-shot one image to 3d object.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Zero-1-to-3: Zero-shot one image to 3d object

Reference 27

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Observation cb137d2c-0f2c-4c85-b328-68cab4500cca · outbound

This paper cites Wonder3D: Single Image to 3D using Cross-Domain Diffusion.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Wonder3D: Single Image to 3D using Cross-Domain Diffusion

Reference 28

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Observation ffb7f258-4d04-4017-8e5c-e82e988bde59 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 29

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Observation cedad0d9-c625-4dd9-a621-d215f04a726d · outbound

This paper cites Decoupled Weight Decay Regularization.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Decoupled Weight Decay Regularization

Reference 30

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Observation d6f22c8d-9106-410c-9b5d-126bd5dc3d51 · outbound

This paper cites Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

Reference 31

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This paper cites An end-to- end transformer model for 3d object detection.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning An end-to- end transformer model for 3d object detection

Reference 32

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Observation 70f384b5-cc7b-4664-9e94-64b567f8d61d · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Masked autoencoders for point cloud self-supervised learning

Reference 33

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Observation a9116193-ecc0-40b2-af28-701ebc7dd9ea · outbound

This paper cites Diffusion autoen- coders: Toward a meaningful and decodable representation.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Diffusion autoen- coders: Toward a meaningful and decodable representation

Reference 34

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Observation 1503f485-51e1-4f6f-959a-e6c2af3c9db3 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 35

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Observation d40ed85e-8b65-481b-89fc-d62c883cebb3 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointnet++: Deep hierarchical feature learning on point sets in a metric space.Advances in neural information processing systems, 30, 2017

Reference 36

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Observation 2289abe0-1bac-4de8-bf60-e2b5d8a9f704 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 37

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Observation 7757d46e-9ba8-474d-83ef-9ad85fd16efa · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointnext: Revisiting pointnet++ with improved training and scaling strategies

Reference 38

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Observation 248b25d1-1542-4232-93e5-212ddbaa79f8 · outbound

This paper cites Learning transferable visual models from natural language supervi- sion.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Learning transferable visual models from natural language supervi- sion

Reference 39

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Observation 6bcf9ebd-e919-4607-a6c8-27547fa3360c · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Exploring the limits of transfer learning with a unified text-to-text transformer.Journal of machine learning research, 21(140):1–67, 2020

Reference 40

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Observation ceb31386-37b1-4328-954a-0afdb27f2c3e · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning High-resolution image synthesis with latent diffusion models

Reference 41

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Observation b9547a7f-5bbf-416a-8296-624be2fb104c · outbound

This paper cites Self-supervised few-shot learning on point clouds.Advances in Neural Information Processing Systems, 33:7212–7221, 2020.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Self-supervised few-shot learning on point clouds.Advances in Neural Information Processing Systems, 33:7212–7221, 2020

Reference 42

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This paper cites MVDream: Multi-view Diffusion for 3D Generation.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning MVDream: Multi-view Diffusion for 3D Generation

Reference 43

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This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Deep unsupervised learning using nonequilibrium thermodynamics

Reference 44

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Observation c94390ee-c1c8-4171-9882-ea89a3f597cf · outbound

This paper cites Epmf: Efficient perception-aware multi-sensor fusion for 3d semantic seg- mentation.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 46(12):8258–8273, 2024.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Epmf: Efficient perception-aware multi-sensor fusion for 3d semantic seg- mentation.IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 46(12):8258–8273, 2024

Reference 45

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This paper cites Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Diffuse, Attend, and Segment: Unsupervised Zero-Shot Segmentation using Stable Diffusion

Reference 46

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Observation e61adc65-3d64-47b0-b4d0-e2b983756779 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:11:14.583484Z

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Observation 29e0b7c9-69b9-4b75-8100-3ff01728982f · outbound

This paper cites Visualizing data using t-sne.Journal of machine learning research, 9 (11), 2008.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Visualizing data using t-sne.Journal of machine learning research, 9 (11), 2008

Reference 48

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Observation 2fe7fb8e-f7d4-471f-9878-d62dcb6b41eb · outbound

This paper cites Dynamic graph cnn for learning on point clouds.ACM Transactions on Graphics (tog), 38(5):1–12, 2019.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Dynamic graph cnn for learning on point clouds.ACM Transactions on Graphics (tog), 38(5):1–12, 2019

Reference 49

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Observation c2119a06-14b0-4db5-b438-2d21d658377e · outbound

This paper cites Pointpatchmix: Point cloud mixing with patch scoring.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointpatchmix: Point cloud mixing with patch scoring

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.943217Z

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Observation 098ad68a-2033-460c-be2d-b0d916872135 · outbound

This paper cites P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.842472Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a7c23fa9-d9e2-45ef-80b6-7054463e85ca · outbound

This paper cites Take-a-photo: 3d-to-2d generative pre-training of point cloud models.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Take-a-photo: 3d-to-2d generative pre-training of point cloud models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.637186Z

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Observation 6907f1aa-268c-4299-8090-52a2dfe58efa · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning 3d shapenets: A deep representation for volumetric shapes

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.531932Z

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Observation c4715f67-a684-46ab-a8f1-10e0d9d177c0 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.424424Z

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Observation 04783218-b9a9-4fed-bebf-262dcea90f5f · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.254793Z

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Observation 857aab2a-324f-4313-a06e-5e1a175b5a85 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Ulip-2: Towards scalable multimodal pre-training for 3d understanding

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.697363Z

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Observation d086ba09-88db-4d52-8c5d-453f971157e5 · outbound

This paper cites Gd-mae: gen- erative decoder for mae pre-training on lidar point clouds.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Gd-mae: gen- erative decoder for mae pre-training on lidar point clouds

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.146285Z

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Observation 2702fef2-ca23-452c-b9b1-28eb39d806c0 · outbound

This paper cites DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:15.836359Z

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Observation b29f7a7f-80da-4892-9dfd-2e47121c104d · outbound

This paper cites Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:10:17.153766Z

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Observation 444bc779-e0af-47e1-99bf-c33aaa53aa0b · outbound

This paper cites A scalable active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning A scalable active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:19.057909Z

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Observation 9d3611d4-4fdb-4d04-805d-1331c51f596f · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.916090Z

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Observation 90f08ac1-a6d5-43ea-b5e6-3cd0cd96dd24 · outbound

This paper cites Exploring Diffusion Time-steps for Unsupervised Representation Learning.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Exploring Diffusion Time-steps for Unsupervised Representation Learning

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:16.121566Z

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Observation 5bbc99fa-d16f-4670-8c07-739437aa0a88 · outbound

This paper cites Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:16.218975Z

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Observation 062c784a-ff7c-46e4-bdbd-769934e37865 · outbound

This paper cites Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training.Advances in neural information processing sys- tems, 35:27061–27074, 2022.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training.Advances in neural information processing sys- tems, 35:27061–27074, 2022

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.751622Z

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Observation 57507315-8fa4-4214-b946-55bc81e2cae1 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.529515Z

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Observation c01214c0-4e5d-4f72-bd94-fd91a4c26d09 · outbound

This paper cites Self-supervised pretraining of 3d features on any point-cloud.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Self-supervised pretraining of 3d features on any point-cloud

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.380825Z

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Observation 86070a7c-ba84-47f5-a481-b37b26ecd42b · outbound

This paper cites Unsupervised rep- resentation learning from pre-trained diffusion probabilistic models.Advances in Neural Information Processing Sys- tems, 35:22117–22130, 2022.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Unsupervised rep- resentation learning from pre-trained diffusion probabilistic models.Advances in Neural Information Processing Sys- tems, 35:22117–22130, 2022

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.272034Z

Source-reported events for the cited work

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Observation 369bdf0b-1b69-494d-9cf5-4987af7f5321 · outbound

This paper cites Unleashing text-to-image diffu- sion models for visual perception.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Unleashing text-to-image diffu- sion models for visual perception

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:18.129125Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99e686ea-f0c3-4f9f-9d28-0e114106b8b5 · outbound

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

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Point cloud pre-training with diffusion models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:17.986753Z

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Observation f5081452-b484-456b-832c-bf82e84b12d6 · outbound

This paper cites As shown in Tab.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning As shown in Tab

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:17.807721Z

Source-reported events for the cited work

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Observation bf580d36-5f05-48f4-a8a7-5103f5272463 · outbound

This paper cites an unresolved cited work.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Unresolved cited work

Reference 71

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:10:17.698514Z

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Observation e6796ec5-8155-40c1-9e4a-6e29b44cb2c0 · outbound

This paper cites Illustration of the Augmentation Strategy.We show our augmentation strategy in Fig.

Harnessing Text-to-Image Diffusion Models for Point Cloud Self-Supervised Learning Illustration of the Augmentation Strategy.We show our augmentation strategy in Fig

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:10:17.529897Z

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