Paper Citation Record · LEDGER

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation

As of 18 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.03300.

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

pith.paper-citation-record.v1
2505.03300 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:58:19.627213Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

35 of 35 outbound references displayed

verified exact6
verified fuzzy14
unresolved14
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ebf5c285-0316-43c2-97d3-0a66144ffbbb · outbound

This paper cites SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.099213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50455245-b026-44f9-809b-aafdb6ce8ede · outbound

This paper cites nuScenes: A multimodal dataset for autonomous driving.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation nuScenes: A multimodal dataset for autonomous driving

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.151474Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edee9cc1-8c49-493b-8013-6da58d5dbc6f · outbound

This paper cites Masked-attention Mask Transformer for Universal Image Segmentation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Masked-attention Mask Transformer for Universal Image Segmentation

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.187248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10819433-da39-41a4-bf48-1200b8b94117 · outbound

This paper cites 4D Spatio- Temporal ConvNets: Minkowski Convolutional Neural Networks, June.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation 4D Spatio- Temporal ConvNets: Minkowski Convolutional Neural Networks, June

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.338274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bbfb490d-7a2c-4dce-a0f2-64fba079846a · outbound

This paper cites Corral-Soto, Mrigank Rochan, Yannis Y.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Corral-Soto, Mrigank Rochan, Yannis Y

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.316058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation aec076a9-6abe-4814-a0c3-0e86312664fe · outbound

This paper cites T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.257000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7448bc5e-d76f-4065-8fb5-ea439af38556 · outbound

This paper cites Learning 3D Semantic Segmentation with only 2D Image Supervision.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Learning 3D Semantic Segmentation with only 2D Image Supervision

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.924411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 687ff0c7-2992-4bbb-bd53-7fe0e69862ec · outbound

This paper cites Seg- ment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Seg- ment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without Manual Labels

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.208312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation edd5fc90-7415-4d4d-a158-936e4dcac199 · outbound

This paper cites Segment Anything.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Segment Anything

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.219629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a4c79148-a6a3-4891-9466-accfabbc2c97 · outbound

This paper cites Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation, March 2019.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation, March 2019

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.162657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3d14bfc5-68e7-4e83-a5dd-d3a0aef69ebc · outbound

This paper cites Pseudo-Label : The Simple and Efficient Semi- Supervised Learning Method for Deep Neural Networks.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Pseudo-Label : The Simple and Efficient Semi- Supervised Learning Method for Deep Neural Networks

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.151071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 27aa2c67-c042-4c7d-affb-cc0dc3c3ab21 · outbound

This paper cites Segment Any Point Cloud Sequences by Distilling Vision Foundation Models.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Segment Any Point Cloud Sequences by Distilling Vision Foundation Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.255235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 682d426d-a174-4186-b970-f44ca0341841 · outbound

This paper cites Swin Transformer: Hierarchical Vision Transformer using Shifted Windows.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.259453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 551c3935-57a6-4e3d-b109-f7d9a7ba3e52 · outbound

This paper cites See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.139481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e80800b2-a995-458f-92a2-5ac591b6b562 · outbound

This paper cites Diffuser: Multi- View 2D-to-3D Label Diffusion for Semantic Scene Segmentation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Diffuser: Multi- View 2D-to-3D Label Diffusion for Semantic Scene Segmentation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.127931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2aaa4543-c2f7-4274-b99c-b34ea0718a8f · outbound

This paper cites SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.868172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 57d1e1ab-09e6-4033-bcb7-294834eee12b · outbound

This paper cites OpenMMLab’s Next-generation Plat- form for General 3D Object Detection, July 2020.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation OpenMMLab’s Next-generation Plat- form for General 3D Object Detection, July 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.116302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 94bbef9d-2c78-4e87-9d13-ac38b326fc96 · outbound

This paper cites OpenMMLab Semantic Segmenta- tion Toolbox and Benchmark, July 2020.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation OpenMMLab Semantic Segmenta- tion Toolbox and Benchmark, July 2020

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.102653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation feff62a3-e5f1-4a1f-a1cc-cd8ede648920 · outbound

This paper cites The Mapillary Vistas Dataset for Semantic Understand- ing of Street Scenes.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation The Mapillary Vistas Dataset for Semantic Understand- ing of Street Scenes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.076934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dcf0bc71-caa3-4619-8684-d8c35ff52495 · outbound

This paper cites Imaging today, foreseeing tomorrow.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Imaging today, foreseeing tomorrow

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.015721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b0ca194a-42e9-4cb4-8e82-3da2eae687ba · outbound

This paper cites Semantic segmentation of mobile mapping point clouds via multi-view label transfer.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Semantic segmentation of mobile mapping point clouds via multi-view label transfer

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:20.003119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8b6c5831-4f57-4aa1-81a7-539037f40a5d · outbound

This paper cites Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentation

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.440431Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce1dd28c-f7a2-4263-b36e-b667202222c4 · outbound

This paper cites Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.444700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10fa0b8-a884-4bfe-ba0c-ea6b67cc598c · outbound

This paper cites Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.809971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ecd28194-2c64-470d-971c-79eaab95af75 · outbound

This paper cites Multi-view classification with convolutional neural networks.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Multi-view classification with convolutional neural networks

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:58:19.991077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4c398470-70d9-4cfa-8d6c-7294a8532e70 · outbound

This paper cites Deep CORAL: Correlation Alignment for Deep Domain Adaptation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Deep CORAL: Correlation Alignment for Deep Domain Adaptation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.463548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74a3b032-4053-4067-9e37-099e90a1012c · outbound

This paper cites KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.778955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ab7e0e0a-7bda-42b9-abe2-4996026d4a8c · outbound

This paper cites ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.495358Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4aea0f3b-bb39-4aab-9db3-4b1cd3a2a74b · outbound

This paper cites LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images

Reference 29

Resolution: metadata mismatch; local_arxiv, observed 2026-08-15T23:58:19.752522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 875ec1a2-fad9-4cbd-bdc6-f5667024c94c · outbound

This paper cites Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation

Reference 30

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.735937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c5638c21-79fc-47c2-9ded-b2e2ded2925c · outbound

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

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Point Transformer V3: Simpler, Faster, Stronger

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.510300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcd00840-4e1e-4b48-b777-2c9384743e77 · outbound

This paper cites ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:58:19.706790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58444a54-78d3-4910-a322-051b8dcab231 · outbound

This paper cites SAM3D: Segment Anything in 3D Scenes.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation SAM3D: Segment Anything in 3D Scenes

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.627213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 472f4d4b-6ed4-43ce-bd32-064c32056034 · outbound

This paper cites 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.196155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e942579-02e9-415a-9323-3adbb798a98b · outbound

This paper cites an unresolved cited work.

3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation Unresolved cited work

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T23:58:19.459711Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.