Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views | Paper Citation Record

Observation 66ae0d3d-0613-493b-a8d9-6c721990e1cc · outbound

This paper cites Learning representations and generative models for 3d point clouds.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Learning representations and generative models for 3d point clouds

Reference 1

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Observation 683722f1-285e-4038-8357-189d990a6b67 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Crosspoint: Self-supervised cross-modal contrastive learning for 3d point cloud understanding

Reference 2

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This paper cites 3d seman- tic parsing of large-scale indoor spaces.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views 3d seman- tic parsing of large-scale indoor spaces

Reference 3

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This paper cites BEiT: BERT Pre-Training of Image Transformers.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views BEiT: BERT Pre-Training of Image Transformers

Reference 4

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Emerg- ing properties in self-supervised vision transformers

Reference 5

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views 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.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pimae: Point cloud and image interactive masked autoencoders for 3d object detection

Reference 7

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pointgpt: Auto-regressively generative pre- training from point clouds

Reference 8

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Observation 17a25566-91ec-4175-b058-f74146aeace3 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views A simple framework for contrastive learning of visual representations

Reference 9

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Reference 10

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Observation 898a68f0-aa87-4912-a024-7957fade8327 · outbound

This paper cites Self-contrastive learning with hard negative sampling for self-supervised point cloud learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Self-contrastive learning with hard negative sampling for self-supervised point cloud learning

Reference 11

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Observation f13b6045-a3c6-4cdd-b1b0-97b0ead96f69 · outbound

This paper cites A comparative review of hand-eye calibration tech- niques for vision guided robots.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views A comparative review of hand-eye calibration tech- niques for vision guided robots

Reference 12

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Observation 84fefa2f-8c88-4327-85a4-e263c533a3eb · outbound

This paper cites Efficient image pre-training with siamese cropped masked autoencoders.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Efficient image pre-training with siamese cropped masked autoencoders

Reference 13

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Observation 26bf6423-87c5-43e9-bb0d-410521f2fe04 · outbound

This paper cites A point set generation network for 3d object reconstruction from a single image.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views A point set generation network for 3d object reconstruction from a single image

Reference 14

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Observation a4bd87a9-5d50-4f04-9aa9-fe2cd5f94ba2 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Revisiting point cloud shape classification with a simple and effective baseline

Reference 15

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Observation cba94cf4-7f97-4012-8800-fb5e621f1da5 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Bootstrap your own latent-a new approach to self-supervised learning

Reference 16

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This paper cites Pct: Point cloud transformer.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pct: Point cloud transformer

Reference 17

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Observation b72c5d02-8db7-4219-a092-f0e4ea5d1041 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training

Reference 18

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Observation f45db9c2-4c5f-4aef-9536-14e73b4c4506 · outbound

This paper cites Siamese masked autoencoders.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Siamese masked autoencoders

Reference 19

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Observation 2756ed16-fa1e-4e21-8f17-36b95e9149c6 · outbound

This paper cites Dynamic focus-aware po- sitional queries for semantic segmentation.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Dynamic focus-aware po- sitional queries for semantic segmentation

Reference 20

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Observation 82b9c782-fce8-41de-8367-995cd5c0b5c3 · outbound

This paper cites Deep residual learning for image recognition.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Deep residual learning for image recognition

Reference 21

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This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Momentum contrast for unsupervised visual rep- resentation learning

Reference 22

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Observation 7594e3f2-5ce3-4e75-bc68-2fb9efe096d9 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Masked autoencoders are scalable vision learners

Reference 23

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This paper cites Distilling the Knowledge in a Neural Network.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Distilling the Knowledge in a Neural Network

Reference 24

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Spatio-temporal self-supervised representation learning for 3d point clouds

Reference 25

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This paper cites Imagenet classification with deep convolutional neural net- works.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Imagenet classification with deep convolutional neural net- works

Reference 26

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This paper cites Semmae: Semantic-guided mask- ing for learning masked autoencoders.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Semmae: Semantic-guided mask- ing for learning masked autoencoders

Reference 27

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This paper cites So-net: Self- organizing network for point cloud analysis.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views So-net: Self- organizing network for point cloud analysis

Reference 28

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This paper cites Cross-BERT for Point Cloud Pretraining.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Cross-BERT for Point Cloud Pretraining

Reference 29

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pointcnn: Convolution on x-transformed points

Reference 30

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Masked dis- crimination for self-supervised learning on point clouds

Reference 31

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Relation-shape convolutional neural network for point cloud analysis

Reference 32

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Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Swin transformer: Hierarchical vision transformer using shifted windows

Reference 33

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Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 34

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Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Decoupled Weight Decay Regularization

Reference 35

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views An end-to- end transformer model for 3d object detection

Reference 36

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This paper cites Representation Learning with Contrastive Predictive Coding.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Representation Learning with Contrastive Predictive Coding

Reference 37

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This paper cites Unsupervised 3d point cloud representation learning by triangle constrained contrast for autonomous driving.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Unsupervised 3d point cloud representation learning by triangle constrained contrast for autonomous driving

Reference 38

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Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Masked autoencoders for point cloud self-supervised learning

Reference 39

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Observation fec18776-c6ad-4d5a-873d-3f43b2141c1c · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 40

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Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 41

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This paper cites Deep hough voting for 3d object detection in point clouds.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Deep hough voting for 3d object detection in point clouds

Reference 42

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This paper cites Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

Reference 43

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This paper cites 3d object detection for autonomous driving: A survey.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views 3d object detection for autonomous driving: A survey

Reference 44

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This paper cites Explore better relative position embeddings from encoding perspective for transformer models.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Explore better relative position embeddings from encoding perspective for transformer models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.980824Z

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This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 46

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This paper cites Info3d: Representation learning on 3d ob- jects using mutual information maximization and contrastive learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Info3d: Representation learning on 3d ob- jects using mutual information maximization and contrastive learning

Reference 47

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Observation fb211cfa-fdaa-4e86-8cd5-e65de49ff8d2 · outbound

This paper cites 3d optical machine vision sensors with intelligent data management for robotic swarm navigation improvement.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views 3d optical machine vision sensors with intelligent data management for robotic swarm navigation improvement

Reference 48

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This paper cites Self-supervised few-shot learning on point clouds.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Self-supervised few-shot learning on point clouds

Reference 49

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Observation a7df20d5-0a65-4b2e-9a91-f3783e2cce06 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 50

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Observation bcbc0289-f1d3-4cb9-b8f5-f2228c1a2c02 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Roformer: Enhanced transformer with rotary position embedding

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T12:47:49.263744Z

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Observation f1b1d5ac-88d4-439c-b79f-98511a41937f · outbound

This paper cites Proxy graph matching with proximal matching networks.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Proxy graph matching with proximal matching networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.922955Z

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Observation ed5001b4-2eb0-410c-b362-30b90e27cca3 · outbound

This paper cites Data pruning via moving- one-sample-out.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Data pruning via moving- one-sample-out

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.911950Z

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Observation 3d7781ea-7024-4eb1-9023-06d4aece3131 · outbound

This paper cites Semantic diffusion network for semantic segmentation.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Semantic diffusion network for semantic segmentation

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.900850Z

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Observation 30b902a1-4cee-4acd-bc03-f6fc8cf92196 · outbound

This paper cites Ensemble quadratic assignment network for graph matching.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Ensemble quadratic assignment network for graph matching

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.890031Z

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Observation d7cb7884-fdad-44e8-aca6-234d027a7fb3 · outbound

This paper cites Saco loss: Sample-wise affinity consistency for vision-language pre-training.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Saco loss: Sample-wise affinity consistency for vision-language pre-training

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.879745Z

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Observation 14d8a9fa-8900-46f8-8961-4774e9325071 · outbound

This paper cites Data pruning by information maximization.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Data pruning by information maximization

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.870660Z

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Observation afb518a5-2abc-4db5-9286-b7fd531bdde1 · outbound

This paper cites Diff-in: Data influence estimation with differential ap- proximation, 2025.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Diff-in: Data influence estimation with differential ap- proximation, 2025

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.861596Z

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Observation 35b7bbb0-24af-4cb2-b85e-4fd84949db58 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.852318Z

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Observation cb609340-1a9e-4652-bd40-fbfd17906020 · outbound

This paper cites Attention is all you need.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Attention is all you need

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.842722Z

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Observation b664ac3d-eb0b-4922-aa3e-7f4d29dd7424 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Unsupervised point cloud pre-training via oc- clusion completion

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.832876Z

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Observation f8be0a0d-96ea-45e0-a3f7-7c7ef0775f6b · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Dynamic graph cnn for learning on point clouds

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.822944Z

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Observation 99bed4fa-da7f-4875-a847-47b47b0e6876 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Take-a-photo: 3d-to-2d generative pre-training of point cloud models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.813244Z

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Observation 8c626494-1fc4-440a-b8ba-2648ce5604e3 · outbound

This paper cites Vertical layering of quantized neural networks for heterogeneous inference.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Vertical layering of quantized neural networks for heterogeneous inference

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.803376Z

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Observation 3bb22e89-44f9-40d6-add4-8759fee6660f · outbound

This paper cites Rethinking and improving relative posi- tion encoding for vision transformer.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Rethinking and improving relative posi- tion encoding for vision transformer

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.794307Z

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Observation a1486870-9bbb-433f-9ce6-fac8021036c5 · outbound

This paper cites Mixture-of- scores: Robust image-text data quality score via three lines of code.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Mixture-of- scores: Robust image-text data quality score via three lines of code

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.784645Z

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Observation b7ef367f-f922-4f51-beaf-dda04db20d0c · outbound

This paper cites Point Transformer V3: Simpler, Faster, Stronger.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Point Transformer V3: Simpler, Faster, Stronger

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-05T12:47:49.316035Z

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Observation 206aaf1b-5c44-4d92-92d1-0a812df08ffc · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views 3d shapenets: A deep representation for volumetric shapes

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.775785Z

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Observation 9063b207-b9e1-4a1a-84c1-b3ba51e8bd57 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.766815Z

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Observation 860e49f5-0ae2-447f-9e5b-4cf2fc185622 · outbound

This paper cites Simmim: A simple framework for masked image modeling.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Simmim: A simple framework for masked image modeling

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.757319Z

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Observation ba365d78-9476-4a0e-81b4-0bd4e4b8431d · outbound

This paper cites To- wards robustness and generalization of point cloud represen- tation: A geometry coding method and a large-scale object- level dataset.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views To- wards robustness and generalization of point cloud represen- tation: A geometry coding method and a large-scale object- level dataset

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.748125Z

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Observation a0b0d257-6c26-4d47-af3e-518b63fa51fe · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.736675Z

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Observation 30543d3e-263c-44c0-872d-45d8494fb173 · outbound

This paper cites XLNet: Generalized Autoregressive Pretraining for Language Understanding.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views XLNet: Generalized Autoregressive Pretraining for Language Understanding

Reference 74

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Observation 1ee533b4-80b9-4798-b7fd-38757e6993b6 · 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.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views A scalable active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.726566Z

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Observation 6a2ee9cf-6e62-4c22-b3ff-b885e6f883aa · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.716338Z

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Observation 2595c51c-a99b-4e3d-bf4b-73029dff76cc · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders

Reference 77

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Observation 9b8ec74b-7409-487e-978e-d11e8de860d5 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.705795Z

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Observation 4524e096-a038-4d51-9e8a-92dfc4976691 · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.695648Z

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Observation 4154174c-8c55-49e1-8348-bd6aa751cbd3 · outbound

This paper cites Cr2pq: Continuous relative rotary positional query for dense visual representa- tion learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Cr2pq: Continuous relative rotary positional query for dense visual representa- tion learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.685433Z

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Observation 5e88c715-7a99-4c79-9b56-368cce011014 · outbound

This paper cites The diversified ensemble neural network.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views The diversified ensemble neural network

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.675392Z

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Observation e0051746-8912-4623-bb44-bb6c841ea018 · outbound

This paper cites Zero-cl: Instance and feature decorrelation 16 for negative-free symmetric contrastive learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Zero-cl: Instance and feature decorrelation 16 for negative-free symmetric contrastive learning

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.665538Z

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Observation bca25c0d-202c-44df-b182-bcc97dc0baaf · outbound

This paper cites M-mix: Generating hard negatives via multi-sample mixing for con- trastive learning.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views M-mix: Generating hard negatives via multi-sample mixing for con- trastive learning

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.655379Z

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Observation 6c1a9e24-a097-441a-9d89-b8c579005fcc · outbound

This paper cites Patch-level contrastive learning via positional query for visual pre-training.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Patch-level contrastive learning via positional query for visual pre-training

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.645507Z

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Observation 9912eb8b-14cc-47b4-83b9-926b643677b5 · outbound

This paper cites Con- textual image masking modeling via synergized contrasting without view augmentation for faster and better visual pre- training.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Con- textual image masking modeling via synergized contrasting without view augmentation for faster and better visual pre- training

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.633263Z

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Observation e7040a5a-e971-40e9-b0b5-f49571ef5cf6 · outbound

This paper cites Continuous-multiple image outpaint- ing in one-step via positional query and a diffusion-based approach.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Continuous-multiple image outpaint- ing in one-step via positional query and a diffusion-based approach

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.623343Z

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Observation e23a0e07-4f84-4e6e-bfa6-141807de5f1f · outbound

This paper cites Pcp- mae: Learning to predict centers for point masked autoen- coders.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Pcp- mae: Learning to predict centers for point masked autoen- coders

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.612833Z

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Observation f99f8211-f240-429d-a4a8-5f1c18ae884b · outbound

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

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views Point cloud pre-training with diffusion models

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.603169Z

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Observation e8b11c97-ea03-4233-86cd-b52c8e97b06f · outbound

This paper cites iBOT: Image BERT Pre-Training with Online Tokenizer.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views iBOT: Image BERT Pre-Training with Online Tokenizer

Reference 89

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Observation 595700ee-69d9-457e-ad22-0393f621f740 · outbound

This paper cites V oxelnet: End-to-end learning for point cloud based 3d object detection.

Towards More Diverse and Challenging Pre-training for Point Cloud Learning: Self-Supervised Cross Reconstruction with Decoupled Views V oxelnet: End-to-end learning for point cloud based 3d object detection

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T12:47:49.591852Z

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