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Paper Citation Record · LEDGER

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation

As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2506.23227.

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

pith.paper-citation-record.v1
2506.23227 v1

Coverage vector

measured 59 of 59 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:52:47.480688Z

measured 59 of 59 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

59 of 59 outbound references displayed

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  • verified fuzzy50
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ee309e64-26c8-4f5d-a67d-c2e6691ac9a5 · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Kpconv: Flexible and deformable convolution for point clouds,

Reference 1

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Observation 1cba00b5-2f80-4c87-827c-a1a6eb6eb9eb · outbound

This paper cites Learning semantic segmentation of large-scale point clouds with random sampling,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Learning semantic segmentation of large-scale point clouds with random sampling,

Reference 2

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Observation 70d08b3c-2f3d-42a0-9e8a-1aa26ab26763 · outbound

This paper cites Cylindrical and asymmetrical 3D convolution net- works for lidar-based perception,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Cylindrical and asymmetrical 3D convolution net- works for lidar-based perception,

Reference 3

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Observation 543405e6-47fd-490c-8831-e3fcf12d1f75 · outbound

This paper cites Point trans- former,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Point trans- former,

Reference 4

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Observation 11d14944-00c8-49e2-997e-008b8c0644e8 · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Point transformer v2: Grouped vector attention and partition-based pooling,

Reference 5

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Observation 6da20b1a-5593-4be3-99bc-2e8a8e3a0581 · outbound

This paper cites OctFormer: Octree-based Transformers for 3D Point Clouds.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation OctFormer: Octree-based Transformers for 3D Point Clouds

Reference 6

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Observation 4d61de3f-66d9-46bc-b02f-924060cab483 · outbound

This paper cites Condaformer: Disassembled transformer with local structure en- hancement for 3D point cloud understanding,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Condaformer: Disassembled transformer with local structure en- hancement for 3D point cloud understanding,

Reference 7

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Source-reported events for the cited work

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Observation 92fa138a-3662-48e7-a67c-93df600f68b8 · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Pointnet: Deep learning on point sets for 3D classification and segmentation,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 87573b4b-1964-4a6f-bf66-fd8fdbcdc38f · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 9

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Source-reported events for the cited work

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Observation c0966d99-bcd2-4893-825d-6d0b4715a200 · outbound

This paper cites Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels,

Reference 10

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Observation 1eae6645-d169-4e97-b963-ab4e1b9d608f · outbound

This paper cites Sqn: Weakly-supervised semantic segmentation of large-scale 3D point clouds,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Sqn: Weakly-supervised semantic segmentation of large-scale 3D point clouds,

Reference 11

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Source-reported events for the cited work

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Observation aab90d71-ce71-44ab-a994-bc630f3d3680 · outbound

This paper cites One thing one click: A self-training approach for weakly supervised 3D semantic segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation One thing one click: A self-training approach for weakly supervised 3D semantic segmentation,

Reference 12

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Observation 8cc8f404-8c78-4556-9665-6a976fb527fc · outbound

This paper cites Perturbed self- distillation: Weakly supervised large-scale point cloud semantic segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Perturbed self- distillation: Weakly supervised large-scale point cloud semantic segmentation,

Reference 13

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Observation db4fce5b-4b5a-4963-98cb-48f78dda6552 · outbound

This paper cites An mil-derived transformer for weakly supervised point cloud segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation An mil-derived transformer for weakly supervised point cloud segmentation,

Reference 14

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Source-reported events for the cited work

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Observation c1aa560c-cdc7-4a7e-aef7-e36d79e12b76 · outbound

This paper cites Cpcm: Contextual point cloud modeling for weakly- supervised point cloud semantic segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Cpcm: Contextual point cloud modeling for weakly- supervised point cloud semantic segmentation,

Reference 15

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Source-reported events for the cited work

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Observation a4f961b9-2739-4d30-b582-da590f1d87bc · outbound

This paper cites All points matter: Entropy-regularized distribution alignment for weakly- supervised 3D segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation All points matter: Entropy-regularized distribution alignment for weakly- supervised 3D segmentation,

Reference 16

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Source-reported events for the cited work

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Observation 5e3132ea-ee88-4c26-9ae5-cd9e766e5c30 · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Scannet: Richly-annotated 3D reconstructions of indoor scenes,

Reference 17

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Observation 404fd53f-2ced-4ccb-8500-d97ccb5ca486 · outbound

This paper cites Multi-path region mining for weakly supervised 3D semantic segmentation on point clouds,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Multi-path region mining for weakly supervised 3D semantic segmentation on point clouds,

Reference 18

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Observation 397db388-06c7-4141-b552-fa51736918c9 · outbound

This paper cites Learning deep features for discriminative localization,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Learning deep features for discriminative localization,

Reference 19

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 29d91779-eeb6-4da4-97cb-cce179f63575 · outbound

This paper cites 3D spatial recognition without spatially labeled 3D,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 3D spatial recognition without spatially labeled 3D,

Reference 20

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Observation 0c8e69c6-b47d-4f60-833e-b820596fd869 · outbound

This paper cites Joint learning of 2D-3D weakly super- vised semantic segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Joint learning of 2D-3D weakly super- vised semantic segmentation,

Reference 21

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation b02099a3-4fa7-421e-a5e2-f2791715e061 · outbound

This paper cites 2D-3D interlaced transformer for point cloud segmentation with scene- level supervision,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 2D-3D interlaced transformer for point cloud segmentation with scene- level supervision,

Reference 22

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 9365f00c-4f1c-4993-a224-ee7ade3dd51a · outbound

This paper cites Dimensionality reduction by learning an invariant mapping,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Dimensionality reduction by learning an invariant mapping,

Reference 23

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Source-reported events for the cited work

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Observation 19cf6ed2-f28a-4924-b23c-fe025bd62eb9 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Representation Learning with Contrastive Predictive Coding

Reference 24

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Source-reported events for the cited work

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Observation e00b1d06-6676-48df-9b30-2ba838ff5fe8 · outbound

This paper cites Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation,

Reference 25

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e46b4be6-376b-4d6b-b3d8-39924c4261be · outbound

This paper cites 3D semantic parsing of large-scale indoor spaces,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 3D semantic parsing of large-scale indoor spaces,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 88971f69-541e-42e2-a9c2-760a497ae9c7 · outbound

This paper cites Weakly supervised semantic segmentation for large-scale point cloud,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Weakly supervised semantic segmentation for large-scale point cloud,

Reference 27

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raw_fallback, observed 2026-08-06T21:52:53.142100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 78d7ba31-a154-4cc5-9aef-561050833016 · outbound

This paper cites Hy- bridcr: Weakly-supervised 3D point cloud semantic segmentation via hybrid contrastive regularization,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Hy- bridcr: Weakly-supervised 3D point cloud semantic segmentation via hybrid contrastive regularization,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 84451605-dff0-461e-8761-ce8285004a07 · outbound

This paper cites Dual adaptive transfor- mations for weakly supervised point cloud segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Dual adaptive transfor- mations for weakly supervised point cloud segmentation,

Reference 29

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 45cc54b3-0ed4-4637-b948-50b08e3c26d5 · outbound

This paper cites Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Weakly Supervised Point Cloud Segmentation via Conservative Propagation of Scene-level Labels

Reference 30

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation d2abecc9-60d3-483d-a30b-582e1c86b603 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Distilling the Knowledge in a Neural Network

Reference 31

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Source-reported events for the cited work

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Observation 045af912-07aa-4622-ad5f-2b3e8ba93426 · outbound

This paper cites Cross- modal learning for domain adaptation in 3D semantic segmen- tation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Cross- modal learning for domain adaptation in 3D semantic segmen- tation,

Reference 32

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raw_fallback, observed 2026-08-06T21:52:52.595428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation 7c8e8a51-a54d-451a-97e2-839b8e6b3ea5 · outbound

This paper cites Cross modal distillation for supervision transfer,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Cross modal distillation for supervision transfer,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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Observation e44a0215-7d7c-44d4-8666-27562dc9f5cb · outbound

This paper cites Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Learning from 2D: Contrastive Pixel-to-Point Knowledge Transfer for 3D Pretraining

Reference 34

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no resolver link, observed 2026-08-06T21:52:45.058273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:52:45.058273Z digest=sha256:96d0789531749ea55177bf86decec8c71014de01c2ac4de14cd980bd2f538891

Observation dc611ad4-d844-4dd7-b711-cedb69317d95 · outbound

This paper cites Image-to-lidar self-supervised distillation for autonomous driv- ing data,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Image-to-lidar self-supervised distillation for autonomous driv- ing data,

Reference 35

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verified fuzzy
raw_fallback, observed 2026-08-06T21:52:52.192195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.180863Z digest=sha256:d6fb3ac8831dc66dea133dc0d3e4d71b735debc28ed192f5dd7dc9fbcbf7dad0

Observation e11a13ff-2e9a-4296-a1aa-ec13d91b20b5 · outbound

This paper cites 2Dpass: 2D priors assisted semantic segmentation on lidar point clouds,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 2Dpass: 2D priors assisted semantic segmentation on lidar point clouds,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:51.919064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.260730Z digest=sha256:b54b21cc096049e5768a93b777313d53ad039a3b3e149f76dde401a499ecc6ff

Observation d504992d-cf38-43ea-9ad2-877ef698fab8 · outbound

This paper cites Camliflow: bidirectional camera-lidar fusion for joint optical flow and scene flow estimation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Camliflow: bidirectional camera-lidar fusion for joint optical flow and scene flow estimation,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:51.592011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.389963Z digest=sha256:83414367f2b1ffb30429be7f1ef7890a7dd5edb5f51c1b32ceca22d5b2a80e02

Observation 124c30fe-3cc4-4989-9de8-e8b13ad14c57 · outbound

This paper cites Bridged transformer for vision and point cloud 3D object detec- tion,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Bridged transformer for vision and point cloud 3D object detec- tion,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:51.305305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.513445Z digest=sha256:a336e70686a9864a49adb90e601a55ab50c92f5de26cdb621942dc89fbda0883

Observation 74b8cdff-5a1e-4050-bad3-6d4573ac52f3 · outbound

This paper cites Bidirectional projection network for cross dimension scene understanding,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Bidirectional projection network for cross dimension scene understanding,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:51.032609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.594409Z digest=sha256:b261d4f8c7b002fedd907b9beb9a2a60fa80a65a9f487d033938832dc769bf21

Observation 4c932bcb-988d-4631-b24e-7074de25f17e · outbound

This paper cites Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Flexmatch: Boosting semi-supervised learning with curriculum pseudo labeling,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:50.753742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.708372Z digest=sha256:32da773953f8dac0e0067833ba635b959d8e9fd684bb29c4a03750c6cf4aeec6

Observation d4fdbf37-d7bd-4649-aa8c-fce3c3c83c5b · outbound

This paper cites Mix3d: Out-of-context data augmentation for 3D scenes,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Mix3d: Out-of-context data augmentation for 3D scenes,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:50.476721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.810635Z digest=sha256:a4e98f0d99dc8539c958150fc882e21ea20b7384c16861f9339a6db5efebbd23

Observation bdfc0503-dba9-4178-8f61-a44daccc58c6 · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 4d spatio-temporal convnets: Minkowski convolutional neural networks,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:50.295828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:45.917034Z digest=sha256:566923e480c4c648c20c6ac05542e42f1ba00107cb1ea65334eba83362ef62e9

Observation aacdc62d-9438-4771-b812-fb126ea9d798 · outbound

This paper cites Deep residual learning for image recognition,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Deep residual learning for image recognition,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:50.074735Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.076531Z digest=sha256:3721783d68861a1dea9c1150726757855f6f45da809622461d16c3332e629342

Observation b788f741-cb51-4a88-8ac9-90e4a6b1e967 · outbound

This paper cites Im- agenet: A large-scale hierarchical image database,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Im- agenet: A large-scale hierarchical image database,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.949563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.144004Z digest=sha256:0deadb4a8772cd2494678ff48309682ee51aaee6de758f6b1213b084f81f7aef

Observation 1fc7d62d-7db8-4168-86bc-0528f87fecf2 · outbound

This paper cites Voxel cloud connectivity segmentation-supervoxels for point clouds,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Voxel cloud connectivity segmentation-supervoxels for point clouds,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.799982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.244735Z digest=sha256:e280eaf5ee7e536aaec352e58fbb369e2c1b09cc3a7a840ceadc1bb3c78cefea

Observation 7119d9a1-d867-4ccc-ae94-21604795567e · outbound

This paper cites Seeded region growing,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Seeded region growing,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.681637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.325980Z digest=sha256:9e2aa106417e98eed18ff481326ebe94ee4ef529c00b124faa7be131de595efd

Observation e986898a-0d84-47e2-8779-8141706de28f · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Momentum contrast for unsupervised visual representation learning,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.539014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.411294Z digest=sha256:298dba2a6cae51af70002b88f26e51ff5c1cd0eefd3db2fb0a3350f62947d035

Observation d4cd9812-7cff-4f1f-8901-cf87b1cf87e0 · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Emerging properties in self-supervised vision transformers,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.424412Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.482917Z digest=sha256:4b61ab04fd2150a8499432d81b45ff002cb0dcf7c41affd249f11ce7edd418c0

Observation a4bc0fa6-7303-4bdb-be8b-572d254a2742 · outbound

This paper cites nuscenes: A mul- timodal dataset for autonomous driving,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation nuscenes: A mul- timodal dataset for autonomous driving,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.312524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.588996Z digest=sha256:79bcfe48e8cff1378819f5cda0f1696a0d5732326f3775cdde0b865368d95f48

Observation ee729f2a-2ff1-4d12-aa7b-b03c16141ba2 · outbound

This paper cites Semantickitti: A dataset for semantic scene understanding of lidar sequences,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Semantickitti: A dataset for semantic scene understanding of lidar sequences,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.199877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.661771Z digest=sha256:97e79520224e4fbe79eb9c0303cbcdf2c4c62304158b549b7fffc7c40bbd78af

Observation 817335fd-b50f-44a9-b69f-a484338da01b · outbound

This paper cites 3D weakly supervised semantic segmentation with 2D vision-language guidance,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation 3D weakly supervised semantic segmentation with 2D vision-language guidance,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:49.028978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.734664Z digest=sha256:ce2828364d51da087002955dffd720133fedd78313e69de41a292017063d35b8

Observation 6629c7c0-fe0c-4e64-b6bf-c7bc4321bca6 · outbound

This paper cites Scaling open-vocabulary image segmentation with image-level labels,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Scaling open-vocabulary image segmentation with image-level labels,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.927778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.856013Z digest=sha256:b408e8e7d3bed83e6c1e2474c217e6316b1509a78b8455be6588add3d7531c47

Observation a3f9b203-b1cf-47bf-89c5-89e848f3ee35 · outbound

This paper cites Language-driven semantic segmentation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Language-driven semantic segmentation,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.806104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:46.951208Z digest=sha256:e6c84d46dc3d0e5dbdf9a8f1015d02c6707ed69f8ed2ab0226155a8715da30f8

Observation f185dc08-73ef-4ba5-adef-d939e81d3edf · outbound

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

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Openscene: 3D scene understanding with open vocabularies,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.695866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.033171Z digest=sha256:cd96591081f51961be827df3ef77767e3982fce1ecbf9b879a058c3ebef8ab1b

Observation a9343c3b-9b88-439a-bccd-ec22c2e058cd · outbound

This paper cites Open vocabulary 3D scene understanding via geometry guided self- distillation,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Open vocabulary 3D scene understanding via geometry guided self- distillation,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.495506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.096075Z digest=sha256:c5f7f3d3fede9f0d6b0d9bfee214f15cb51fb7728a8d607abd2afbfa586f673e

Observation 9602deb5-befb-4cfd-9ec7-399f1d53b60e · outbound

This paper cites Open-vocabulary 3D semantic segmentation with foundation models,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Open-vocabulary 3D semantic segmentation with foundation models,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.339949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.171534Z digest=sha256:1d2f06dfb691dd37f78372a5913f8d02fef2adb017fa750ab3b7046101c2c055

Observation f07f53bc-d50f-4c78-91a3-05326b652708 · outbound

This paper cites Segment anything,.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Segment anything,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.196797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.267068Z digest=sha256:7eeb1b4bf3ad0c19fa3936fdb16b27310f086087ea9612b2ab22cd857d96bcbb

Observation 14efb435-9680-4ca9-9851-54904a9b66f0 · outbound

This paper cites For instance, ‘drivable surface’, ‘manmade’, and ‘veg- etation’ constitute over 70% of all points, while rare classes like ‘bicycle’ or ‘motorcycle’ represent less than 0.1%.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation For instance, ‘drivable surface’, ‘manmade’, and ‘veg- etation’ constitute over 70% of all points, while rare classes like ‘bicycle’ or ‘motorcycle’ represent less than 0.1%

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T21:52:48.021637Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.357154Z digest=sha256:722b97ed383b1ae0fe6f850a763f1a1e36d79fc3f00a69ce3445e5d229b233ab

Observation cacb44f2-80f9-469c-bb2b-4250ade1ee39 · outbound

This paper cites Baseline.

High-quality Pseudo-labeling for Point Cloud Segmentation with Scene-level Annotation Baseline

Reference 59

Resolution
malformed identifier
raw_fallback, observed 2026-08-06T21:52:47.867592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T21:52:47.480688Z digest=sha256:ed0748d748e528c3ee3d0577f006e029904c7e0a1b952a7df701f8ee31372678

Pith citing papers

No inbound Pith citation observations are available.