Paper Citation Record · LEDGER

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 3 inbound Pith citation observations for arXiv:2506.21957.

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

pith.paper-citation-record.v1
2506.21957 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:20:14.069361Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:31:41.370563Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T23:25:07.140371Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8ba7bb73-9fc3-43f4-aaba-e4797652cf07 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Crosspoint: Self- supervised cross-modal contrastive learning for 3d point cloud understanding

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:08.556087Z

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Observation ac8d38c4-1ed5-417b-8920-f1b6236ac12b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 7

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Observation 2b43d32f-ebfa-481b-8297-87df83dad239 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding A point set generation network for 3d object re- construction from a single image

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:08.252790Z

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Observation e2c99fe2-8452-4e3b-8838-7c4efee7b4b8 · outbound

This paper cites Masked au- toencoders are scalable vision learners.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Masked au- toencoders are scalable vision learners

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:08.147899Z

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Observation 59358a84-da07-40d3-b252-8430abaeea76 · outbound

This paper cites PointMamba: A Simple State Space Model for Point Cloud Analysis.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding PointMamba: A Simple State Space Model for Point Cloud Analysis

Reference 11

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Observation 503bb6f6-7a2a-49e7-a2e6-3eb74960aa4f · outbound

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Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Unresolved cited work

Reference 14

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Observation 148e6f00-007b-4da2-b690-d3eb40e2dc6d · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Con- trast with reconstruct: Contrastive 3d representation learn- ing guided by generative pretraining

Reference 15

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Observation 4d8e1a93-3418-44a9-9ebe-7487fc669654 · outbound

This paper cites Improving language understanding by generative pre-training,.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Improving language understanding by generative pre-training,

Reference 16

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Observation f2c73376-23c5-4cb4-ad2b-f86022954a4a · outbound

This paper cites Language models are unsupervised multitask learners.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Language models are unsupervised multitask learners

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:16.565989Z

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Observation 5853e609-a090-4a04-91c0-73e312a66134 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Geomae: Masked geometric target prediction for self-supervised point cloud pre-training

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:16.387329Z

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Observation f1209a16-a319-4377-87f8-e80994edc1c4 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Revis- iting point cloud classification: A new benchmark dataset and classification model on real-world data

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:16.234216Z

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Observation 49d62e45-a8a4-4c76-98f9-7d70a8b6646d · outbound

This paper cites Extract- ing and composing robust features with denoising autoen- coders.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Extract- ing and composing robust features with denoising autoen- coders

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:16.076337Z

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Observation 8de57264-5a56-4b5d-930e-9b48b722f015 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding 3d shapenets: A deep representation for volumetric shapes

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:15.845857Z

Source-reported events for the cited work

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Observation a768a161-dd6a-44d9-9400-c7d23c240d91 · outbound

This paper cites Self-supervised intra-modal and cross- modal contrastive learning for point cloud understanding.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Self-supervised intra-modal and cross- modal contrastive learning for point cloud understanding

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:15.660157Z

Source-reported events for the cited work

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Observation d27fad98-f57f-42c5-9190-91cde6ba8b5b · outbound

This paper cites Point- contrast: Unsupervised pre-training for 3d point cloud un- derstanding.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Point- contrast: Unsupervised pre-training for 3d point cloud un- derstanding

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:15.418231Z

Source-reported events for the cited work

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Observation 7dc2f1d7-b917-4352-a781-a7aae950f488 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Gd-mae: generative decoder for mae pre-training on lidar point clouds

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:15.152137Z

Source-reported events for the cited work

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Observation 81ae61b4-84b6-4ae2-ac6d-5ef3d4f27200 · outbound

This paper cites A scalable active framework for region annotation in 3d shape collec- tions.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding A scalable active framework for region annotation in 3d shape collec- tions

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:14.992350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4e7ec1c7-c400-4469-8121-7a96e0286783 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Self-supervised pretraining of 3d features on any point-cloud

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:14.639623Z

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Observation dd0fd758-38f6-465b-a1b1-dc4e87c359f0 · outbound

This paper cites Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Parameter is Not All You Need: Starting from Non-Parametric Networks for 3D Point Cloud Analysis

Reference 30

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Observation 683f1a9e-6d97-4182-85c4-15548c68f966 · outbound

This paper cites Point transformer.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Point transformer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:14.286687Z

Source-reported events for the cited work

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Observation 7954ce3d-f118-4cef-ada6-3260725848a8 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Dynamic graph cnn for learning on point clouds

Reference 2008

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:15.977335Z

Source-reported events for the cited work

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Observation ab7f0872-79d4-45e4-a3bf-1b25bc2f299e · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Pimae: Point cloud and image inter- active masked autoencoders for 3d object detection

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:08.422798Z

Source-reported events for the cited work

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Observation 5ec59c64-3a05-48fb-b073-91c5cded0076 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Point-bert: Pre- training 3d point cloud transformers with masked point modeling

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:14.800770Z

Source-reported events for the cited work

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Observation 053a538d-73dd-4067-8caa-b0ffb0610635 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 2017

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Observation 60b8db98-808f-4d2b-92bb-dc362a975a9f · outbound

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Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Unresolved cited work

Reference 2018

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Source-reported events for the cited work

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Observation 7cf3e5e2-c63d-4494-b5f2-33c255223ef4 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Masked discrimination for self-supervised learning on point clouds

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:07.867252Z

Source-reported events for the cited work

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Observation 05889191-1373-42bb-98f1-9a89538a701f · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding ShapeNet: An Information-Rich 3D Model Repository

Reference 2020

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Source-reported events for the cited work

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Observation a89fcf7d-b8ee-4c3d-a9f0-1b1e66253ec9 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Point-m2ae: multi-scale masked autoen- coders for hierarchical point cloud pre-training

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:20:14.486731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 88c88f1d-7c37-4a72-af34-21fd7612ef74 · outbound

This paper cites Language models are few-shot learners.

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Language models are few-shot learners

Reference 2022

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Source-reported events for the cited work

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Observation 5a038ff1-0935-41dc-8a1f-0adf30de8841 · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T22:20:12.788364Z

Source-reported events for the cited work

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Observation 4737523c-5de4-4fc2-9729-be1611a084ae · outbound

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

Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding Relation-shape convolutional neu- ral network for point cloud analysis

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:21:07.974314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning cites this paper.

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

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T22:31:41.370563Z

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Observation ea0bda78-cdd4-41b5-8479-17a0cf2c6eda · 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 Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T02:20:55.037531Z

Source-reported events for the cited work

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Observation 5726b0d4-86ee-473b-986e-0c3f17a040b9 · 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 Exploring Semantic Masked Autoencoder for Self-supervised Point Cloud Understanding

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-30T23:25:07.141993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.