Paper Citation Record · LEDGER

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning

As of 7 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 2 inbound Pith citation observations for arXiv:2506.21541.

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

pith.paper-citation-record.v1
2506.21541 v3

Coverage vector

measured 54 of 54 reference resolution

Typed states for the displayed outbound observations.

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measured 56 of 56 standing notices

One-hop event checks from named stored sources.

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measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T23:24:11.495687Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:25:07.137424Z

Reference resolution

54 of 54 outbound references displayed

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External citation measurements

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Outbound references

Observation 8edd43d2-72f6-4d4d-8e40-ee00f0ad5178 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning ShapeNet: An Information-Rich 3D Model Repository

Reference 1

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Observation 345981ce-e2d5-4d23-bc93-20d6a63b66be · outbound

This paper cites Decoupled local aggregation for point cloud learning, 2023.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Decoupled local aggregation for point cloud learning, 2023

Reference 2

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Observation 3b2ff6a8-bc8c-4a6c-9ae7-f4efe32b4a4d · outbound

This paper cites PointGPT: Auto-regressively Generative Pre-training from Point Clouds.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning PointGPT: Auto-regressively Generative Pre-training from Point Clouds

Reference 3

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Observation 9669fb5a-1d3b-469d-bd6d-eb5aa51d6df0 · outbound

This paper cites 3d point cloud processing and learning for autonomous driving: Impacting map cre- ation, localization, and perception.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning 3d point cloud processing and learning for autonomous driving: Impacting map cre- ation, localization, and perception

Reference 4

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Observation ff6c3804-01a7-445a-beb8-2aff97ac4943 · outbound

This paper cites Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

Reference 5

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Observation 0f6fce27-1146-4710-a5ff-78ece887278e · outbound

This paper cites BERT: pre-training of deep bidirectional trans- formers for language understanding.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning BERT: pre-training of deep bidirectional trans- formers for language understanding

Reference 6

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Observation 9e8ca49c-2dcd-48e6-b96b-83fe6670900c · outbound

This paper cites an unresolved cited work.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Unresolved cited work

Reference 7

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Observation f3c2cb83-b46d-4773-909d-79ea79f3eb7f · outbound

This paper cites Pranet: Parallel reverse attention network for polyp segmentation.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pranet: Parallel reverse attention network for polyp segmentation

Reference 8

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Observation 876c75f1-6240-4d38-8bc7-a66920c78a8c · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 9

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Observation 3157c9ac-243e-4ebc-b0fd-7e9ed1e8426f · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Efficiently Modeling Long Sequences with Structured State Spaces

Reference 10

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Observation 6720fd70-be34-4617-94a5-da4752b64ee5 · outbound

This paper cites Combining recurrent, convolutional, and continuous-time models with linear state space layers.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Combining recurrent, convolutional, and continuous-time models with linear state space layers

Reference 11

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Observation dd3911b8-0940-423a-b12e-928d21a24296 · outbound

This paper cites Pct: Point cloud transformer.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pct: Point cloud transformer

Reference 12

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Observation 7b330d62-7f74-4c7f-9def-6b4a6e14d900 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Deep learning for 3d point clouds: A survey

Reference 13

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Observation b66b8a6a-133d-4cbb-8cfd-c6d6bc5fe5eb · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training

Reference 14

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Observation bd274685-7216-46a4-9457-f715931d3d79 · outbound

This paper cites Mamba3d: Enhancing local features for 3d point cloud anal- ysis via state space model.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Mamba3d: Enhancing local features for 3d point cloud anal- ysis via state space model

Reference 15

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Observation fd0c7ae5-786b-426d-9087-9fdd4a3b21ac · outbound

This paper cites Masked autoencoders are scalable vision learners.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Masked autoencoders are scalable vision learners

Reference 16

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Observation d06191d0-0ea6-42cf-84eb-0756efd7a239 · outbound

This paper cites A new approach to linear filter- ing and prediction problems.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning A new approach to linear filter- ing and prediction problems

Reference 17

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Observation f888597c-91cf-48f6-822f-872d2318f992 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Adam: A Method for Stochastic Optimization

Reference 18

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Observation 83667a67-8333-4f0b-8df5-645967789b91 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointcnn: Convolution on x-transformed points

Reference 19

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Observation 750d1234-8075-4d5b-aac0-bb0bc54348a3 · outbound

This paper cites Deep learning for lidar point clouds in autonomous driving: A review.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Deep learning for lidar point clouds in autonomous driving: A review

Reference 20

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Observation 9549cee4-2a93-4c64-a705-2b6bc8fbb3f1 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointmamba: A simple state space model for point cloud analysis

Reference 21

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Observation 96ba058f-1093-4ccf-93ed-0e9e690fdf5d · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Masked dis- crimination for self-supervised learning on point clouds

Reference 22

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Observation b1e11df5-5b7e-432a-b38d-2768e6d911aa · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 23

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Observation d007897b-2b1e-4038-a718-713fc3088251 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

Reference 24

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Observation 4f8add24-3e74-4652-897b-471dd7f808c4 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Masked autoencoders for point cloud self-supervised learning

Reference 25

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Observation 58e16762-df50-4a17-b194-8bc96d2acb35 · outbound

This paper cites A review of point cloud registration algorithms for mobile robotics.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning A review of point cloud registration algorithms for mobile robotics

Reference 26

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Observation 4510964c-2734-4e7e-a816-4f73c6d11cc2 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 27

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Observation 73a9f6ff-0073-4754-adf7-a1a41ed56126 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 28

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Observation 067f2f69-04ed-4004-b21e-3543f4412b55 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 29

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Observation 4a73dd95-2631-4b85-9070-ab4ce5ae87d3 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointnext: Revisiting pointnet++ with improved training and scaling strategies

Reference 30

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Observation ca63934a-ab27-4e36-93cc-3b8f8562002a · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Kpconv: Flexible and deformable convolution for point clouds

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:45.313901Z

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Observation 5eafab24-b69b-44a7-932a-d6764f1cbb84 · outbound

This paper cites Long-short range adap- tive transformer with dynamic sampling for 3d object detec- tion.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Long-short range adap- tive transformer with dynamic sampling for 3d object detec- tion

Reference 32

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Observation a7884e41-6441-4619-88d9-b031358b4223 · outbound

This paper cites Rethinking masked representation learning for 3d point cloud understanding.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Rethinking masked representation learning for 3d point cloud understanding

Reference 33

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Observation 7973a255-4e7b-4a06-b786-23a1a233b5c0 · outbound

This paper cites State space model meets transformer: A new paradigm for 3d object detection.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning State space model meets transformer: A new paradigm for 3d object detection

Reference 34

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Observation 617f86b3-8d9a-4790-b0b8-d3dd7d151a77 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Dynamic graph cnn for learning on point clouds

Reference 35

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Observation 3dbf4712-7f55-45ba-871f-5481366253e4 · outbound

This paper cites Attention-based point cloud edge sampling.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Attention-based point cloud edge sampling

Reference 36

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Observation 349911d9-e858-4937-b153-35f5f3adf599 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point transformer v2: Grouped vector atten- tion and partition-based pooling

Reference 37

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Observation a1d8706f-bdc3-4113-8cb7-74c32eba57b8 · outbound

This paper cites Point transformer v3: Simpler faster stronger.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point transformer v3: Simpler faster stronger

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:44.453538Z

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Observation 78156107-8e53-4363-b71e-9a6c5c884417 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning 3d shapenets: A deep representation for volumetric shapes

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:44.371699Z

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Observation 9d0eb208-0219-462c-b811-dd3efe3db32b · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:44.256547Z

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Observation d5066687-8e43-4a74-a85e-1a765e5b6a06 · 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.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning A scalable active framework for region annotation in 3d shape collections.ACM Transactions on Graphics (ToG), 35(6):1–12, 2016

Reference 41

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Observation 582913e5-7b58-4c3f-bf74-52ba3bcfc385 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:44.048917Z

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Observation e5fad45d-8551-43a3-8250-61340bcc867d · outbound

This paper cites Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

Reference 43

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Observation c8cdd0b2-4014-46fa-9ac4-0431f7458069 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 44

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Observation 3ae3f995-5ff8-4806-acf9-8bbac41190e3 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 45

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Observation 7f980dad-aa9f-4da3-b439-257a3a39e3d7 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:43.828087Z

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Observation 97aa9995-17fb-4713-8b12-0cd13e098104 · outbound

This paper cites Point Cloud Mamba: Point Cloud Learning via State Space Model.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point Cloud Mamba: Point Cloud Learning via State Space Model

Reference 47

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Observation 9c2906a2-a944-42fe-bb84-280f72d5c623 · outbound

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Self-supervised pretraining of 3d features on any point-cloud

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:43.728294Z

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Observation a6ee970e-962b-4839-9b66-4488fd137470 · outbound

This paper cites Point transformer.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Point transformer

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:43.616858Z

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Observation 436b1653-b306-4aa2-82f9-1067b53e3e9e · outbound

This paper cites an unresolved cited work.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:31:43.506627Z

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Observation a335e21a-ab5c-4f6d-8f11-d4e2bb8baf43 · outbound

This paper cites Structural SSM Block In the state-wise update strategy and sequence-length adap- tive strategy, we modify the SSM parameter generation pro- cess to enhance its efficiency.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Structural SSM Block In the state-wise update strategy and sequence-length adap- tive strategy, we modify the SSM parameter generation pro- cess to enhance its efficiency

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:43.328469Z

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Observation 5867727c-6157-45f4-b3a7-d075d395c7ba · outbound

This paper cites Detailed Results on Part Segmentation We present per-category part segmentation results on the ShapeNetPart [41] dataset.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Detailed Results on Part Segmentation We present per-category part segmentation results on the ShapeNetPart [41] dataset

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:43.167284Z

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Observation 713613ef-e156-446d-956e-759b9e8242c9 · outbound

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StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Unresolved cited work

Reference 53

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Observation b1a70456-332c-409c-a3e1-f56253475be0 · outbound

This paper cites Our primary goal is to fully exploit the potential of Mamba for point cloud rep- resentation learning.

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning Our primary goal is to fully exploit the potential of Mamba for point cloud rep- resentation learning

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:31:42.808189Z

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Pith citing papers

Observation 0be928ea-4813-4f03-9221-c532724a6525 · inbound

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth cites this paper.

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning

Reference 9

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Observation 2adc82a9-78e6-4fc2-8811-7c685bb9ba2d · inbound

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth cites this paper.

FS-I2P:A Hierarchical Focus-Sweep Registration Network with Dynamically Allocated Depth StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:25:07.139197Z

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