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Paper Citation Record · LEDGER

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation

As of 19 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2509.13907.

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

pith.paper-citation-record.v1
2509.13907 v3

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T16:33:37.289633Z

measured 38 of 38 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

38 of 38 outbound references displayed

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Outbound references

Observation dd5a46dc-231a-42b3-b2ac-9e76fefc10fd · outbound

This paper cites Correlations Are Ruining Your Gradient Descent.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Correlations Are Ruining Your Gradient Descent

Reference 1

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Observation 940e7d51-e15b-4eaf-b788-2f08067532b3 · outbound

This paper cites Re- thinking few-shot 3d point cloud semantic segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Re- thinking few-shot 3d point cloud semantic segmentation

Reference 2

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Observation 5727f87f-0153-4bf3-94eb-8ca73ca69ea7 · outbound

This paper cites Multimodality helps few-shot 3d point cloud semantic seg- mentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Multimodality helps few-shot 3d point cloud semantic seg- mentation

Reference 3

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Observation 20f3e05f-1428-4f79-8196-c6089863f23b · outbound

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

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation 3d seman- tic parsing of large-scale indoor spaces

Reference 4

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Observation 57f971ea-ed85-476f-bdf5-8a4278bfe824 · outbound

This paper cites independent components.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation independent components

Reference 5

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Observation cf4e6de8-2bea-4c52-b809-51e2db621b31 · outbound

This paper cites Deep learning on 3d semantic segmentation: A detailed review.Remote Sensing, 17(2):298, 2025.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Deep learning on 3d semantic segmentation: A detailed review.Remote Sensing, 17(2):298, 2025

Reference 6

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Observation b2e73c8a-45c7-4ef1-ba6d-38cb22bf8f27 · outbound

This paper cites End-to- end object detection with transformers.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation End-to- end object detection with transformers

Reference 7

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Observation 5d3a9c22-da02-42b7-9da9-73737c5c0a29 · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Masked-attention mask transformer for universal image segmentation

Reference 8

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Observation 9ef1e462-ac9b-4b5f-ab1b-14d50ef09e80 · outbound

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

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 9

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Observation e2a6ffb2-8cb8-4a1e-8247-9eef625206ce · outbound

This paper cites Batch normalization prov- ably avoids ranks collapse for randomly initialised deep net- works.Advances in Neural Information Processing Systems, 33:18387–18398, 2020.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Batch normalization prov- ably avoids ranks collapse for randomly initialised deep net- works.Advances in Neural Information Processing Systems, 33:18387–18398, 2020

Reference 10

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Observation 80a894d0-1683-4483-b0fe-6ca433dfc9c8 · outbound

This paper cites Attention is not all you need: Pure attention loses rank dou- bly exponentially with depth.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Attention is not all you need: Pure attention loses rank dou- bly exponentially with depth

Reference 11

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Observation 91d68125-1c2f-4f52-bf35-5c1fc4d604dc · outbound

This paper cites Self- support few-shot semantic segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Self- support few-shot semantic segmentation

Reference 12

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Observation 524db85c-df3f-4177-a508-cd0c2dad9952 · outbound

This paper cites Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation.IEEE Transactions on Image Processing, 2023.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Prototype adaption and projection for few- and zero-shot 3d point cloud semantic segmentation.IEEE Transactions on Image Processing, 2023

Reference 13

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Observation 4b412d98-4e1d-452b-b06d-ca09299f1b86 · outbound

This paper cites Decorre- lated batch normalization.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Decorre- lated batch normalization

Reference 14

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Observation e14cbe90-5a3e-4e33-8e93-0bc15c8b934a · outbound

This paper cites Revealing the Dark Secrets of BERT.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Revealing the Dark Secrets of BERT

Reference 15

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Observation 4278e6d7-a031-4c0c-9edd-d05e11df5a3a · outbound

This paper cites Stratified trans- former for 3d point cloud segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Stratified trans- former for 3d point cloud segmentation

Reference 16

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Observation c936d96d-b918-419d-81bf-4733db7c206c · outbound

This paper cites Samplenet: Differentiable point cloud sampling.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Samplenet: Differentiable point cloud sampling

Reference 17

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Observation b395ea1a-94d3-4a09-9cb6-2b4ad3d491ff · outbound

This paper cites Activating self- attention for multi-scene absolute pose regression.Advances in Neural Information Processing Systems, 37:38508–38529,.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Activating self- attention for multi-scene absolute pose regression.Advances in Neural Information Processing Systems, 37:38508–38529,

Reference 18

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Observation 4bec39e7-8d49-4221-870a-b05b2a0a1971 · outbound

This paper cites Temporal alignment-free video matching for few- shot action recognition.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Temporal alignment-free video matching for few- shot action recognition

Reference 19

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Observation 96b6d606-8b7f-4034-b03b-8222c3e3a08d · outbound

This paper cites Localization and expansion: A decoupled frame- work for point cloud few-shot semantic segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Localization and expansion: A decoupled frame- work for point cloud few-shot semantic segmentation

Reference 20

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Observation 0e53844c-1166-4142-a7a6-96beda73203a · outbound

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

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Masked dis- crimination for self-supervised learning on point clouds

Reference 21

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Observation 43c2feb5-fd85-4283-8340-b8e58fd394fb · outbound

This paper cites Part-aware prototype network for few-shot semantic segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Part-aware prototype network for few-shot semantic segmentation

Reference 22

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Observation 089ac701-0232-47d3-90cc-4b964b00a0dd · outbound

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

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation An end-to- end transformer model for 3d object detection

Reference 23

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Observation 2fdf0b76-a385-4ae7-bc33-6b800379d5d0 · outbound

This paper cites Boosting few-shot 3d point cloud segmentation via query-guided enhancement.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Boosting few-shot 3d point cloud segmentation via query-guided enhancement

Reference 24

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Observation d5f4d46a-b1d7-4c65-a1fa-562bd99eec3b · outbound

This paper cites How does batch normalization help optimiza- tion?Advances in neural information processing systems, 31, 2018.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation How does batch normalization help optimiza- tion?Advances in neural information processing systems, 31, 2018

Reference 25

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Observation 8d00d220-cff9-449b-986e-9bcdb3d99f76 · outbound

This paper cites Mask3D: Mask Transformer for 3D Semantic Instance Segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Mask3D: Mask Transformer for 3D Semantic Instance Segmentation

Reference 26

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Observation f6cc9774-fde1-407b-9141-9390c2fcc189 · outbound

This paper cites Prototypical networks for few-shot learning.Advances in neural informa- tion processing systems, 30, 2017.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Prototypical networks for few-shot learning.Advances in neural informa- tion processing systems, 30, 2017

Reference 27

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This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 28

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This paper cites Matching networks for one shot learning.Ad- vances in neural information processing systems, 29, 2016.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Matching networks for one shot learning.Ad- vances in neural information processing systems, 29, 2016

Reference 29

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This paper cites Detr3d: 3d ob- ject detection from multi-view images via 3d-to-2d queries.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Detr3d: 3d ob- ject detection from multi-view images via 3d-to-2d queries

Reference 30

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This paper cites Unsupervised point cloud rep- resentation learning with deep neural networks: A survey.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Unsupervised point cloud rep- resentation learning with deep neural networks: A survey

Reference 31

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Observation d2192f11-1968-4a61-9ca2-5d0b7c9b9f4e · outbound

This paper cites Pixel-aligned recurrent queries for multi-view 3d object detection.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Pixel-aligned recurrent queries for multi-view 3d object detection

Reference 32

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Observation 2dd9e018-f32b-4262-b559-afd52f9f428a · outbound

This paper cites Stabilizing transformer training by pre- venting attention entropy collapse.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Stabilizing transformer training by pre- venting attention entropy collapse

Reference 33

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Observation 893fbcbf-f83a-4ccc-86c2-8e95b1b158df · outbound

This paper cites Feature- proxy transformer for few-shot segmentation.Advances in neural information processing systems, 35:6575–6588,.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Feature- proxy transformer for few-shot segmentation.Advances in neural information processing systems, 35:6575–6588,

Reference 34

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This paper cites Threshold-Consistent Margin Loss for Open-World Deep Metric Learning.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Threshold-Consistent Margin Loss for Open-World Deep Metric Learning

Reference 35

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Observation 0dc3bab5-9617-46a5-958b-20c57bc9e15b · outbound

This paper cites Few-shot 3d point cloud semantic segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Few-shot 3d point cloud semantic segmentation

Reference 36

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Observation 0b51bcfc-1365-42f2-aa3e-ae9193c2f2fd · outbound

This paper cites Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis

Reference 37

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source=pdf_text observed=2026-08-04T16:33:37.237715Z digest=sha256:c405c37fbceb15603641ad5ac8330957a9d30ba7d7ebb9d939e1151e18194edf

Observation e493f401-6835-42b5-ab7b-6b4c10461295 · outbound

This paper cites No time to train: Empowering non-parametric net- works for few-shot 3d scene segmentation.

White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation No time to train: Empowering non-parametric net- works for few-shot 3d scene segmentation

Reference 38

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no resolver link, observed 2026-08-04T16:33:37.289633Z

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source=pdf_text observed=2026-08-04T16:33:37.289633Z digest=sha256:8fe7a3ceaa5bb0eccd3f792631fc1b8b6868ff8645dc87bd962fe2d21b2d229f

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