Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding | Paper Citation Record

Observation f601a621-b8f1-436c-b778-145e8e648eba · outbound

This paper cites Segment anything.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Segment anything

Reference 1

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Observation 3091c7cb-beb7-4184-8dc0-f1ab3ac97057 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding SAM 2: Segment Anything in Images and Videos

Reference 2

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Observation 0da3052e-4c77-4225-8593-6419a2bfca14 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Learning transferable visual models from natural language supervision

Reference 3

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Observation 542f138f-57ea-4b7f-bccc-dea0c7b817b8 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Emerging properties in self-supervised vision transformers

Reference 4

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Observation 16647114-b818-4f5b-9545-95bf2881885a · outbound

This paper cites an unresolved cited work.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Unresolved cited work

Reference 5

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Observation 49b504cb-8c35-4970-9d64-fe4a41d23991 · outbound

This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Reference 6

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Observation 2f61ef11-4b75-49f3-b783-4005e377f692 · outbound

This paper cites Perla: Perceptive 3d language assistant.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Perla: Perceptive 3d language assistant

Reference 7

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Observation 8f4994a1-a916-4fed-b541-e32d49f4b879 · outbound

This paper cites Shapesplat: A large-scale dataset of gaussian splats and their self-supervised pretraining.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Shapesplat: A large-scale dataset of gaussian splats and their self-supervised pretraining

Reference 8

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Observation f79611db-3256-4c8e-b75d-bf0bdc7c4aba · outbound

This paper cites SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding SceneSplat: Gaussian Splatting-based Scene Understanding with Vision-Language Pretraining

Reference 9

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Observation 760f88c6-8039-457b-a77e-253ec5fcd8d6 · outbound

This paper cites Unsupervised deep probabilistic approach for partial point cloud registration.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Unsupervised deep probabilistic approach for partial point cloud registration

Reference 10

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Observation 1b5031e9-1e10-4b84-9a7c-f7452697f650 · outbound

This paper cites Pvafn: Point-voxel attention fusion network with multi-pooling enhancing for 3d object detection.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pvafn: Point-voxel attention fusion network with multi-pooling enhancing for 3d object detection

Reference 11

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Observation ac7e62f0-7dc8-4b6b-b1fd-1435e367d0dc · outbound

This paper cites ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding ZeroReg: Zero-Shot Point Cloud Registration with Foundation Models

Reference 12

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Observation 29e71aa9-3b02-428e-afbb-1958cf4ca6ec · outbound

This paper cites Attention is all you need.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Attention is all you need

Reference 13

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Observation 34842a55-e66c-4bd2-a0d3-a518b3b9b48b · outbound

This paper cites Masked jigsaw puzzle: A versatile position embedding for vision transformers.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Masked jigsaw puzzle: A versatile position embedding for vision transformers

Reference 14

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Observation 5c275d38-db8c-4d56-ba6f-e3b7facec82a · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding An image is worth 16x16 words: Transformers for image recognition at scale

Reference 15

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Observation ec305381-9e04-4924-9614-35cff6cee61d · outbound

This paper cites Point transformer.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point transformer

Reference 16

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Observation 343dde7d-6275-43c1-8597-56ab67415b1e · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point transformer v2: Grouped vector attention and partition-based pooling

Reference 17

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Observation 5f2cb2c8-d54c-4dc7-9897-9428332de9ee · outbound

This paper cites Point transformer v3: Simpler faster stronger.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point transformer v3: Simpler faster stronger

Reference 18

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Observation ce3abd1b-e9fe-43dc-8bd4-014a095e7f27 · outbound

This paper cites Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Bringing masked autoencoders explicit contrastive properties for point cloud self-supervised learning

Reference 19

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Observation 114a15ae-ef4d-4d31-8d01-848fdb2ed881 · outbound

This paper cites Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point-DAE: Denoising Autoencoders for Self-supervised Point Cloud Learning

Reference 20

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Observation 4eb5dbf1-a71b-47c1-995d-ceed029b1531 · outbound

This paper cites Geomae: Masked geometric target prediction for self-supervised point cloud pre-training.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Geomae: Masked geometric target prediction for self-supervised point cloud pre-training

Reference 21

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Observation f1e2362a-dae5-4d4e-9f7f-e966245e8d7d · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Masked autoencoders for point cloud self-supervised learning

Reference 22

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Observation ce473609-e824-4e84-b030-d5c440340a28 · outbound

This paper cites Bootstrap your own latent: A new approach to self-supervised learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Bootstrap your own latent: A new approach to self-supervised learning

Reference 24

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Observation c28429f7-220a-41e9-82f9-456d7b3ddeb7 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Momentum contrast for unsupervised visual representation learning

Reference 25

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Observation d2e3f62f-6ad0-4b1f-b83d-9f7ef26212e9 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:20.022788Z

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Observation 18eb51ed-0036-4e49-97be-dcf66a15d4e5 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Masked autoencoders are scalable vision learners

Reference 27

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Observation ea258da3-3ec5-44dc-818f-14ffdd3bb96b · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding ShapeNet: An Information-Rich 3D Model Repository

Reference 28

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Observation 6aebc049-a809-4157-b2d9-e86cb7ea6b10 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.734983Z

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Observation 4af60b0d-d21f-4050-9bd5-e64017e0901a · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Con- trast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.612751Z

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Observation 6dd51cc3-a9c2-4417-865f-2ce40df02a8c · outbound

This paper cites Frozen clip transformer is an efficient point cloud encoder.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Frozen clip transformer is an efficient point cloud encoder

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.444272Z

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Observation 58f66f49-378d-40a7-ac64-23db889454a1 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pimae: Point cloud and image interactive masked autoencoders for 3d object detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.312052Z

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Observation 0a10ceb8-d9ce-4f61-859a-aaad75086616 · outbound

This paper cites Pointmamba: A simple state space model for point cloud analysis.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointmamba: A simple state space model for point cloud analysis

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.200186Z

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Observation ca2fc097-a46a-4922-8d8e-ddacd6c9f7da · outbound

This paper cites Pix4point: image pretrained standard transformers for 3d point cloud understanding.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pix4point: image pretrained standard transformers for 3d point cloud understanding

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:19.029943Z

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Observation 71e4f928-2f15-47ee-8e47-037bb4db8d74 · outbound

This paper cites Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Can We Solve 3D Vision Tasks Starting from A 2D Vision Transformer?

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:31:08.536346Z

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Observation 0498613c-fa76-4360-a121-b6b1fab7f1d5 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointcontrast: Unsupervised pre-training for 3d point cloud understanding

Reference 36

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Observation bf8d5040-b357-4467-95ca-227767b43892 · outbound

This paper cites Spatio-temporal self-supervised representation learning for 3d point clouds.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Spatio-temporal self-supervised representation learning for 3d point clouds

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:18.632037Z

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Observation ef262909-64fe-41e5-81e8-08b00408ceb2 · outbound

This paper cites Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:18.480563Z

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Observation 86b64999-99cb-4918-aa7b-15084c3f4f40 · outbound

This paper cites Point discrimi- native learning for data-efficient 3d point cloud analysis.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point discrimi- native learning for data-efficient 3d point cloud analysis

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:18.256799Z

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Observation aebf0e7b-c1c1-417a-a0a6-2199ab4e23de · outbound

This paper cites Data augmentation-free unsupervised learning for 3d point cloud understanding.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Data augmentation-free unsupervised learning for 3d point cloud understanding

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:18.134556Z

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Observation a2e0def2-a139-48ce-af44-39555e7f42c6 · outbound

This paper cites Unsupervised point cloud representation learning by clustering and neural rendering.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Unsupervised point cloud representation learning by clustering and neural rendering

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:17.938629Z

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Observation 444bd5b0-85d8-44d6-af2f-02be00988940 · outbound

This paper cites Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:17.718583Z

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Observation 3980f775-c2f9-4d3d-813e-29ee31b199c3 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Gd-mae: generative decoder for mae pre-training on lidar point clouds

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:17.431440Z

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Observation 7977a6e6-e4c7-458a-ba7e-82b5e6072d14 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointgpt: Auto- regressively generative pre-training from point clouds

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:17.207800Z

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Observation c93181fb-3bca-4156-9d84-8f11ef0e3520 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point cloud pre-training with diffusion models

Reference 45

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Observation eac6e71e-5e4b-4223-ab54-fd26cf812987 · outbound

This paper cites Denoising diffusion probabilistic models.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Denoising diffusion probabilistic models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:16.671042Z

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Observation 911764cd-a211-4ef1-a5f1-3cfdeadc89d8 · outbound

This paper cites Spatio-temporal graph diffusion for text-driven human motion generation.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Spatio-temporal graph diffusion for text-driven human motion generation

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:16.427327Z

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Observation f2de19b8-ae44-4d8e-a1d3-dbc559960e65 · outbound

This paper cites Denoising diffusion probabilistic models for action-conditioned 3d motion generation.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Denoising diffusion probabilistic models for action-conditioned 3d motion generation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:16.139201Z

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Observation ffc74efe-c326-4579-9189-d59f841077e5 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Efficiently modeling long sequences with structured state spaces

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:15.940840Z

Source-reported events for the cited work

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Observation 61625a85-baa3-4226-bdbe-02a0a0a9849c · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding LoRA: Low-Rank Adaptation of Large Language Models

Reference 50

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Observation 9e9c116f-f003-4c08-8360-566912aeb108 · outbound

This paper cites Parameter-efficient transfer learning for nlp.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Parameter-efficient transfer learning for nlp

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:15.751618Z

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Observation 05e90377-a106-4812-9f7b-a64acc369604 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Adaptformer: Adapting vision transformers for scalable visual recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:15.573985Z

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Observation 5ed91843-9620-4b79-b3ee-540ebacddac3 · outbound

This paper cites Visual prompt tuning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Visual prompt tuning

Reference 53

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Observation b0e658ee-898a-427e-9772-cbf0b22680da · outbound

This paper cites Exploring sparse visual prompt for domain adaptive dense prediction.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Exploring sparse visual prompt for domain adaptive dense prediction

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:15.280623Z

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Observation 1baac4a4-0667-4996-be69-6955ba6213c8 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point-peft: Parameter-efficient fine-tuning for 3d pre-trained models

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:15.111658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 68f3f28d-e32f-4823-9c75-1f391001e74a · outbound

This paper cites Gaprompt: Geometry-aware point cloud prompt for 3d vision model.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Gaprompt: Geometry-aware point cloud prompt for 3d vision model

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.988583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fdb3093-5059-4acf-adb3-4568ac43c35c · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:02.918450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 789bd28e-c386-4c5d-ae78-3aeea2d6c50d · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:03.077802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0730824-d762-4294-a85b-1679243c5df9 · outbound

This paper cites Language models are unsupervised multitask learners.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Language models are unsupervised multitask learners

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:03.206959Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfdfcd6e-3106-4a00-b803-273515f27447 · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Training data-efficient image transformers & distillation through attention

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.821934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0a6ad1d-e84c-4249-8d2c-30989aefec82 · outbound

This paper cites AST: Audio Spectrogram Transformer.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding AST: Audio Spectrogram Transformer

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:03.520260Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7cea26ac-4cf3-477a-9764-7611f6df012b · outbound

This paper cites Ssast: Self-supervised audio spectrogram transformer.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Ssast: Self-supervised audio spectrogram transformer

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.674060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87198369-ad0e-4b68-9f9b-8dfc69140e0a · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Imagebind: One embedding space to bind them all

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.528061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e690621-f026-44ed-a64d-e3820e529b0c · outbound

This paper cites Pointclip: Point cloud understanding by clip.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointclip: Point cloud understanding by clip

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.414825Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d4f75a62-210c-402f-a5ea-b8c6681a3d01 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.314200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 94e6ee63-7215-4850-bba1-4f3aea878fc0 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.168323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2ea0b34d-2abd-473e-8828-b8a791275990 · outbound

This paper cites Image2point: 3d point-cloud understand- ing with 2d image pretrained models.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Image2point: 3d point-cloud understand- ing with 2d image pretrained models

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:14.060148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 75a276ea-305f-434d-bb40-67d08cba74c7 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:04.494724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9ee25db-96c0-4245-b971-7038ee1c698e · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.955946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a9748a4e-b614-42ea-a503-5354ddc573fc · outbound

This paper cites Point Cloud GAN.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point Cloud GAN

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:04.711501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14d1b597-b42e-4c36-914c-b579972c597f · outbound

This paper cites Large-scale point cloud semantic segmentation with superpoint graphs.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Large-scale point cloud semantic segmentation with superpoint graphs

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.858926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ac327fa-b1bb-44f3-9f0f-cdec396b5fc0 · outbound

This paper cites Open3D: A Modern Library for 3D Data Processing.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Open3D: A Modern Library for 3D Data Processing

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:04.896833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d394341-382f-44fa-8a90-8bee41170ccf · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointnext: Revisiting pointnet++ with improved training and scaling strategies

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.752251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 99825e7d-ad8f-452c-8fad-ea6bd7389744 · outbound

This paper cites Investigating self-supervised methods for label-efficient learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Investigating self-supervised methods for label-efficient learning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.611873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9e4f9fd3-2583-46af-8722-16a71c0d5c23 · outbound

This paper cites Sinkhorn distances: Lightspeed computation of optimal transport.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Sinkhorn distances: Lightspeed computation of optimal transport

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.491294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28138aac-6cc3-4975-814e-59fabdbbc063 · outbound

This paper cites Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Open3dis: Open-vocabulary 3d instance segmentation with 2d mask guidance

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.315313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7faa0cba-343f-49d5-a972-ab9d242d10fc · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Pointclip v2: Adapting clip for powerful 3d open-world learning

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:13.160276Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e4a377a2-62d1-4f50-9655-6c70395cd150 · outbound

This paper cites Adamw and super-convergence is now the fastest way to train neural nets.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Adamw and super-convergence is now the fastest way to train neural nets

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:12.893549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bfe268e0-70b4-4f9d-b6d4-09c47cb52d63 · outbound

This paper cites Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Zamir, Helen Jiang, Ioannis Brilakis, Martin Fischer, and Silvio Savarese

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:12.709788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 01e67b11-7a48-445a-a1a5-ce1cc6aa8d6b · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Partdistill: 3d shape part segmentation by vision-language model distillation

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:12.481481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a810e214-03c8-4547-905e-8487d685716d · outbound

This paper cites Maskclip: Masked self-distillation advances contrastive language-image pretraining.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Maskclip: Masked self-distillation advances contrastive language-image pretraining

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:12.208669Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 53680c93-4084-4a8f-8f03-b729c09e8532 · outbound

This paper cites Clip-fo3d: Learning free open-world 3d scene representations from 2d dense clip.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Clip-fo3d: Learning free open-world 3d scene representations from 2d dense clip

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:11.976053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c8142e72-550d-4625-a425-b0da7fac18d7 · outbound

This paper cites Openscene: 3d scene understanding with open vocabularies.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Openscene: 3d scene understanding with open vocabularies

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:11.758045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0faf4c34-4914-40a5-9204-4430f0d37b13 · outbound

This paper cites Geometrically-driven aggregation for zero-shot 3d point cloud understanding.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Geometrically-driven aggregation for zero-shot 3d point cloud understanding

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:11.556463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8a750a7-0b91-4765-aeb2-b2dc3197e3a5 · outbound

This paper cites Cus3d: Clip-based unsupervised 3d segmentation via object-level denoise.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Cus3d: Clip-based unsupervised 3d segmentation via object-level denoise

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:11.244506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3dfdcbdc-e185-4dc7-a04a-2d9f166a272e · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding 3d shapenets: A deep representation for volumetric shapes

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:11.001973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 73a06799-2662-45ac-b295-20f60a72e594 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:10.740107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87b5d476-d0cd-4e47-a5e8-e32425b6345c · outbound

This paper cites Vconv-dae: Deep volumetric shape learning without object labels.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Vconv-dae: Deep volumetric shape learning without object labels

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:10.534535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7bba723b-3816-4e40-b358-f49efd7c79fe · outbound

This paper cites PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-07T14:31:06.584313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c75da201-77e5-4e02-9ad6-d3f7c5f9dbbc · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Point-bert: Pre- training 3d point cloud transformers with masked point modeling

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:20.714521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 13a1e11d-f80a-4ab3-a6ad-183bcfe33d6d · outbound

This paper cites Masked discrimination for self-supervised learning on point clouds.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Masked discrimination for self-supervised learning on point clouds

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:10.262890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c03a7de-fa71-42c5-b8ec-128dd0eba52f · outbound

This paper cites Masked Surfel Prediction for Self-Supervised Point Cloud Learning.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Masked Surfel Prediction for Self-Supervised Point Cloud Learning

Reference 92

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:31:07.774994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b7acd45-e3fd-4e4c-b0e3-6aaf3fe37ff0 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Towards compact 3d representations via point feature enhancement masked autoencoders

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:10.038456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 592ca33c-53f7-4c3c-ac42-0fb60a4d7661 · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:09.782559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ed4aa4f-1760-4115-9eef-de4175e85b0b · outbound

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

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding A scalable active framework for region annotation in 3d shape collections

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:09.563356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f8750294-1275-4aed-a0c6-3b8c9be49ec0 · outbound

This paper cites Unsupervised point cloud pre-training via occlusion completion.

Self-Supervised and Generalizable Tokenization for CLIP-Based 3D Understanding Unsupervised point cloud pre-training via occlusion completion

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:31:09.306619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers