Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T05:29:16.942433Z
Paper Citation Record · LEDGER
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds
As of 8 August 2026, this Paper Citation Record lists 95 of 95 outbound references and 0 inbound Pith citation observations for arXiv:2506.07857.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
95 of 95 outbound references displayed
- verified exact2
- verified fuzzy66
- unresolved26
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 8ef8caf5-2417-4e36-b858-7688ffc037d7 · outbound
This paper cites Seeded region growing.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Seeded region growing
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:04.672123Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c2740df0-0a12-4ab2-9e8d-e624c2b5d3d9 · outbound
This paper cites Joint 2D-3D-Semantic Data for Indoor Scene Understanding.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Joint 2D-3D-Semantic Data for Indoor Scene Understanding
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:04.747208Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 36033516-118d-4263-8f4d-ed5b97fe6a53 · outbound
This paper cites nuScenes: A multimodal dataset for autonomous driving.CVPR, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds nuScenes: A multimodal dataset for autonomous driving.CVPR, 2020
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:04.893299Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 52434921-783d-4a65-8ecb-622f40cf891b · outbound
This paper cites Deep Clustering for Unsupervised Learn- ing of Visual Features.ECCV, 2018.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Deep Clustering for Unsupervised Learn- ing of Visual Features.ECCV, 2018
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.003561Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c8613782-ff9a-4d61-8e40-8f7dfeecbf48 · outbound
This paper cites Emerg- ing Properties in Self-Supervised Vision Transformers.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Emerg- ing Properties in Self-Supervised Vision Transformers
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.193910Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20a824fa-14f2-4a30-ad07-8c87dca3a5b7 · outbound
This paper cites CLIP2Scene: Towards Label-efficient 3D Scene Un- derstanding by CLIP.CVPR, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds CLIP2Scene: Towards Label-efficient 3D Scene Un- derstanding by CLIP.CVPR, 2023
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.351288Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3942dba6-c100-4d33-80d6-d5d2dc11ed80 · outbound
This paper cites Shape Self-Correction for Unsupervised Point Cloud Understanding.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Shape Self-Correction for Unsupervised Point Cloud Understanding.ICCV, 2021
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.436229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08109e7e-d277-4c4d-863f-0b59fcb37eb0 · outbound
This paper cites Point DC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super- V oxel Clustering.ICCV, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Point DC: Unsupervised Semantic Segmentation of 3D Point Clouds via Cross-modal Distillation and Super- V oxel Clustering.ICCV, 2023
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.524426Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcf75135-193d-4599-8b14-de443fe17183 · outbound
This paper cites Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation Using Bounding Boxes
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.632134Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf96271d-8454-486d-8cc4-a223e36bb5ca · outbound
This paper cites PiCIE: Unsupervised Semantic Segmentation us- ing Invariance and Equivariance in Clustering.CVPR, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PiCIE: Unsupervised Semantic Segmentation us- ing Invariance and Equivariance in Clustering.CVPR, 2021
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.709841Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d5d38066-9b05-411e-a574-1563c60a5609 · outbound
This paper cites 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neu- ral Networks.CVPR, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neu- ral Networks.CVPR, 2019
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:05.795699Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bc9f10fd-87d3-4110-bb43-dec40dfd88e9 · outbound
This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks.CVPR, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds 4d spatio-temporal convnets: Minkowski convolutional neural networks.CVPR, 2019
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.020498Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a605255f-5305-49b7-9f32-1ab6d32c8143 · outbound
This paper cites Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.139891Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 37f5b035-de97-4dcd-b5cb-e7409f52221f · outbound
This paper cites PLA: Language-Driven Open- V ocabulary 3D Scene Understanding.CVPR, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PLA: Language-Driven Open- V ocabulary 3D Scene Understanding.CVPR, 2023
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.314518Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 821e56e9-62cb-4e57-a071-88020deea089 · outbound
This paper cites Unsupervised Semantic Segmentation by Con- trasting Object Mask Proposals.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised Semantic Segmentation by Con- trasting Object Mask Proposals.ICCV, 2021
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.469414Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cd21ac50-829b-4119-a3f0-8cd63c354dbc · outbound
This paper cites 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks.CVPR, 2018.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks.CVPR, 2018
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.587827Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation eb29b289-19b3-4d88-8c71-2d85e7eceb8e · outbound
This paper cites SAM-guided Graph Cut for 3D Instance Segmentation.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds SAM-guided Graph Cut for 3D Instance Segmentation.ECCV, 2024
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.698601Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 839759bc-a016-4a70-b494-5ee94621f846 · outbound
This paper cites Martin, and Shi-Min Hu.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Martin, and Shi-Min Hu
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:29.172613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9c3e91ee-4cfc-4d7a-8cf2-69f62c0216e3 · outbound
This paper cites Semantic Abstraction: Open- World 3D Scene Understanding from 2D Vision-Language Models.CoRL, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Semantic Abstraction: Open- World 3D Scene Understanding from 2D Vision-Language Models.CoRL, 2022
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:06.992404Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e127fce-fcff-4833-a218-129f61d5a314 · outbound
This paper cites an unresolved cited work.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unresolved cited work
Reference 20
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:29:29.045435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ce8cf1fc-2da3-4a6c-932f-3292d7046904 · outbound
This paper cites Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts.CVPR, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Exploring Data-Efficient 3D Scene Understanding with Contrastive Scene Contexts.CVPR, 2021
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.926811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e141446f-71f8-411e-8ddf-b9a26183271a · outbound
This paper cites RandLA-Net: Efficient Semantic Segmentation of Large- Scale Point Clouds.CVPR, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds RandLA-Net: Efficient Semantic Segmentation of Large- Scale Point Clouds.CVPR, 2020
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.829376Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation df885995-de65-4aa8-922e-ea5e2b440fd2 · outbound
This paper cites Learning Semantic Segmentation of Large-scale Point Clouds with Random Sampling.TPAMI, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Learning Semantic Segmentation of Large-scale Point Clouds with Random Sampling.TPAMI, 2021
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.714848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 880a61ff-7c85-4a40-bcad-87597510f549 · outbound
This paper cites Exploring the devil in graph spectral domain for 3d point cloud attacks.ECCV,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Exploring the devil in graph spectral domain for 3d point cloud attacks.ECCV,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.596770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 050d2c5b-74be-4365-aeff-f321390950e4 · outbound
This paper cites SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds.ECCV, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds.ECCV, 2022
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.456556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7ecf06b4-5e1e-4bf0-b38f-4e38177462ad · outbound
This paper cites Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds.ICCV, 2021
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.327179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7a776979-6d80-4124-ba35-790b9d121707 · outbound
This paper cites Invariant infor- mation clustering for unsupervised image classification and segmentation.ICCV, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Invariant infor- mation clustering for unsupervised image classification and segmentation.ICCV, 2019
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.183607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 16b3b40e-b3ad-4c9c-822e-319737d5887d · outbound
This paper cites EAGLE: Eigen Aggregation Learning for Object- Centric Unsupervised Semantic Segmentation.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds EAGLE: Eigen Aggregation Learning for Object- Centric Unsupervised Semantic Segmentation.CVPR, 2024
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:28.049865Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 110faf59-9c7c-4aee-b99a-ac522c387b1c · outbound
This paper cites On-the-fly Category Discovery for LiDAR Semantic Segmentation.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds On-the-fly Category Discovery for LiDAR Semantic Segmentation.ECCV, 2024
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.877486Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2a5ef01e-64de-4887-aba7-21513ee41af8 · outbound
This paper cites Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Berg, Wan-Yen Lo, Piotr Doll ´ar, and Ross Girshick
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.708217Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 19bcf2ae-c995-450e-b625-7474a51a4b29 · outbound
This paper cites OneFormer3D: One Transformer for Unified Point Cloud Segmentation.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds OneFormer3D: One Transformer for Unified Point Cloud Segmentation.CVPR, 2024
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.510916Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5cd55171-99a7-45fe-a7cf-e6fce66fa807 · outbound
This paper cites Virtual multi-view fusion for 3D semantic segmentation.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Virtual multi-view fusion for 3D semantic segmentation
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.382497Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e4ad29e8-5111-4d3b-a365-dac9075841e5 · outbound
This paper cites Large-scale point cloud semantic segmentation with superpoint graphs.CVPR,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Large-scale point cloud semantic segmentation with superpoint graphs.CVPR,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.233358Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c25deb71-0662-4c17-8c74-8de23b8a47f9 · outbound
This paper cites Octree guided CNN with Spherical Kernels for 3D Point Clouds.CVPR,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Octree guided CNN with Spherical Kernels for 3D Point Clouds.CVPR,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:27.079455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation bb404c6d-8fc6-4d17-9d51-64ae0d746008 · outbound
This paper cites Uni3DL: A Unified Model for 3D Vision-Language Understanding.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Uni3DL: A Unified Model for 3D Vision-Language Understanding.ECCV, 2024
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:26.934712Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d2752c35-6e0e-4a1e-9207-6b0116b6788e · outbound
This paper cites PointCNN: Convolution On X- Transformed Points.NIPS, 2018.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PointCNN: Convolution On X- Transformed Points.NIPS, 2018
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:26.761804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f7caab5a-1da0-4108-a2f2-ee03504298b4 · outbound
This paper cites Breckon, and Hubert P.H.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Breckon, and Hubert P.H
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:26.628301Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2aaee0bc-b0c4-4d79-90cf-3bdc35193936 · outbound
This paper cites an unresolved cited work.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unresolved cited work
Reference 38
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:29:26.477110Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 308197d7-eb72-41ad-b2d6-986450f991e6 · outbound
This paper cites LESS: Label-Efficient Seman- tic Segmentation for LiDAR Point Clouds.ECCV, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds LESS: Label-Efficient Seman- tic Segmentation for LiDAR Point Clouds.ECCV, 2022
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:26.274924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 6cea6937-91d7-465a-b547-fae4e207937d · outbound
This paper cites Segment Any Point Cloud Sequences by Distilling Vision Foundation Models.NeurIPS, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Segment Any Point Cloud Sequences by Distilling Vision Foundation Models.NeurIPS, 2023
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:09.648133Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43ae2924-f6d1-4533-91d0-c2d05a026890 · outbound
This paper cites Point- V oxel CNN for Efficient 3D Deep Learning.NeurIPS, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Point- V oxel CNN for Efficient 3D Deep Learning.NeurIPS, 2019
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:26.120211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a5c23ef6-8736-4b1d-9ed7-84559cf85b81 · outbound
This paper cites One Thing One Click: A Self-Training Approach for Weakly Super- vised 3D Semantic Segmentation.CVPR, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds One Thing One Click: A Self-Training Approach for Weakly Super- vised 3D Semantic Segmentation.CVPR, 2021
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.986978Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a8c7b6ec-eeab-43f0-8d9a-34ab004610c9 · outbound
This paper cites VV-Net: V oxel V AE Net with Group Convolu- tions for Point Cloud Segmentation.ICCV, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds VV-Net: V oxel V AE Net with Group Convolu- tions for Point Cloud Segmentation.ICCV, 2019
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.846044Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7abf4a15-5499-41c7-acdb-61fa6be8e650 · outbound
This paper cites RangeNet++: Fast and Accurate LiDAR Semantic Segmentation.IROS, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds RangeNet++: Fast and Accurate LiDAR Semantic Segmentation.IROS, 2019
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.676871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation a90083f1-1723-4688-9261-2d1feacea7a7 · outbound
This paper cites Unsupervised semantic segmentation of urban high-density multispectral point clouds.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised semantic segmentation of urban high-density multispectral point clouds
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T05:29:17.502962Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 3f653e3c-84ff-4833-bc62-1c70f8b5fb30 · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.TMLR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds DINOv2: Learning Robust Visual Features without Supervision.TMLR, 2024
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.508165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c6fc6fee-3a16-4520-b6a3-92aefb0830f5 · outbound
This paper cites Better Call SAL: Towards Learning to Segment Anything in Lidar.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Better Call SAL: Towards Learning to Segment Anything in Lidar
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.370134Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ae952e4c-759d-4c4a-b9b1-e363d5cd3086 · outbound
This paper cites Autore- gressive Unsupervised Image Segmentation.ECCV, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Autore- gressive Unsupervised Image Segmentation.ECCV, 2020
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:25.224079Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 11db57eb-e66b-4a74-b4c1-47703e36d225 · outbound
This paper cites an unresolved cited work.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unresolved cited work
Reference 49
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:29:25.023103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 052378e4-ea86-44f6-8e16-d5aaaa0e3454 · outbound
This paper cites V oxel cloud connectivity segmentation- supervoxels for point clouds.CVPR, 2013.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds V oxel cloud connectivity segmentation- supervoxels for point clouds.CVPR, 2013
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.844794Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f444869f-cb71-4ffe-a5c6-b986b76c85a2 · outbound
This paper cites OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmenta- tion.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds OA-CNNs: Omni-Adaptive Sparse CNNs for 3D Semantic Segmenta- tion.CVPR, 2024
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.690467Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 17bd4329-f58a-4e0e-9a32-113609b4ee70 · outbound
This paper cites OpenScene: 3D Scene Understanding with Open V ocabular- ies.CVPR, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds OpenScene: 3D Scene Understanding with Open V ocabular- ies.CVPR, 2023
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.544196Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 45f64317-5376-4e03-ac30-05f752be3d15 · outbound
This paper cites Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Learning to Adapt SAM for Segmenting Cross-Domain Point Clouds.ECCV, 2024
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.382311Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c3ec29a7-e85b-4951-9287-ff40173bb158 · outbound
This paper cites Qi, Hao Su, Kaichun Mo, and Leonidas J.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Qi, Hao Su, Kaichun Mo, and Leonidas J
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.213710Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8731977e-804a-4839-981b-0075bb0414cf · outbound
This paper cites Qi, Li Yi, Hao Su, and Leonidas J.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Qi, Li Yi, Hao Su, and Leonidas J
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:24.029868Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1a1452e9-9e57-4f4d-a796-2d7bd5207a87 · outbound
This paper cites Learning Transferable Visual Models From Natural Language Supervision.ICML, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Learning Transferable Visual Models From Natural Language Supervision.ICML, 2021
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:23.878455Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 7201cf75-3424-4305-9da6-0273cc1531a0 · outbound
This paper cites Global-Local Bidi- rectional Reasoning for Unsupervised Representation Learn- ing of 3D Point Clouds.CVPR, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Global-Local Bidi- rectional Reasoning for Unsupervised Representation Learn- ing of 3D Point Clouds.CVPR, 2020
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:23.732769Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9be0910e-1195-4ae2-82ca-4f5daa334a99 · outbound
This paper cites SAM 2: Segment Anything in Images and Videos.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds SAM 2: Segment Anything in Images and Videos
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:11.816389Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 141e4321-54df-4d9a-83fe-5f0fb2bbff4b · outbound
This paper cites Effi- cient 3d semantic segmentation with superpoint transformer.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Effi- cient 3d semantic segmentation with superpoint transformer
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:23.533260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1405e4b1-07d0-41f7-9b47-0dde5843c96c · outbound
This paper cites Language- Grounded Indoor 3D Semantic Segmentation in the Wild.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Language- Grounded Indoor 3D Semantic Segmentation in the Wild
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:23.359564Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8eadd7b3-3589-4f09-b273-0d1d0082f6d6 · outbound
This paper cites Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Reference 61
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T05:29:17.303170Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation db5c2183-99c0-47f2-be53-9447839f7d68 · outbound
This paper cites Discrete signal processing on graphs: Graph fourier transform.ICASSP,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Discrete signal processing on graphs: Graph fourier transform.ICASSP,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:23.200140Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b5d31217-c464-4451-9bac-714d1a0e1fb5 · outbound
This paper cites Self-Supervised Deep Learning on Point Clouds by Reconstructing Space.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Self-Supervised Deep Learning on Point Clouds by Reconstructing Space
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:22.961624Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2bd90a8e-725b-479f-a163-3a64001258b1 · outbound
This paper cites Mask3D: Mask Trans- former for 3D Semantic Instance Segmentation.ICRA, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Mask3D: Mask Trans- former for 3D Semantic Instance Segmentation.ICRA, 2023
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:22.789322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 67eab7bb-69f9-4080-babf-291e5e060086 · outbound
This paper cites Progressive Proxy Anchor Propagation for Unsuper- vised Semantic Segmentation.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Progressive Proxy Anchor Propagation for Unsuper- vised Semantic Segmentation.ECCV, 2024
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:22.561460Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e0ab5aaf-7a3b-427e-b309-eb342a66eccc · outbound
This paper cites Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation.CVPR, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Weakly Supervised Segmentation on Outdoor 4D point clouds with Temporal Matching and Spatial Graph Propagation.CVPR, 2022
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:22.355950Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation f59836fa-e289-4afe-9128-6758d42db59e · outbound
This paper cites Canonical Capsules: Unsupervised Capsules in Canoni- cal Pose.NeurIPS, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Canonical Capsules: Unsupervised Capsules in Canoni- cal Pose.NeurIPS, 2021
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:22.178474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 862810e3-3a3a-4633-ab1d-cd6782020f50 · outbound
This paper cites Searching efficient 3d archi- tectures with sparse point-voxel convolution.ECCV, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Searching efficient 3d archi- tectures with sparse point-voxel convolution.ECCV, 2020
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:21.968005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 526d8692-0bfb-4ea0-bf74-ac533c43ff80 · outbound
This paper cites an unresolved cited work.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unresolved cited work
Reference 69
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:29:21.765056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 76feed93-8321-4958-8d7a-2557b82f559a · outbound
This paper cites Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:21.505172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 564f59a7-ed8f-4ad2-83c0-d35cbe360826 · outbound
This paper cites KPConvX: Modernizing Kernel Point Con- volution with Kernel Attention.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds KPConvX: Modernizing Kernel Point Con- volution with Kernel Attention.CVPR, 2024
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:21.372645Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 99be0566-5df7-4c50-b891-1b1b7addce7e · outbound
This paper cites Unsupervised Point Cloud Co-part Segmentation via Co-attended Superpoint Generation and Aggregation.TMM,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised Point Cloud Co-part Segmentation via Co-attended Superpoint Generation and Aggregation.TMM,
Reference 72
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:21.188377Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b5d67843-f4ee-4277-82b3-d7371f9401ea · outbound
This paper cites Scribble- Supervised LiDAR Semantic Segmentation.CVPR, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Scribble- Supervised LiDAR Semantic Segmentation.CVPR, 2022
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:20.981800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 11fd413e-ae4d-4fb9-9e54-15008d2e46d3 · outbound
This paper cites an unresolved cited work.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unresolved cited work
Reference 74
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T05:29:20.822776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 8da40b6f-f5b9-468e-bf8a-ee45b4d301d2 · outbound
This paper cites Sarma, Michael M.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Sarma, Michael M
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:20.674342Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 5e32bcfb-5b6b-4577-b710-679cfd9972b8 · outbound
This paper cites Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:20.453073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation c077255d-2ec0-4d68-836f-c85705dae036 · outbound
This paper cites SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D Li- DAR Point Cloud.ICRA, 2018.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D Li- DAR Point Cloud.ICRA, 2018
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:20.291751Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d98834a6-64d1-455f-9c64-fe98d897d2a1 · outbound
This paper cites PointConv: Deep Convolutional Networks on 3D Point Clouds.CVPR, 2019.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PointConv: Deep Convolutional Networks on 3D Point Clouds.CVPR, 2019
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:20.154821Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 90a1b976-f8c3-4ea5-89a6-b46be2d459f0 · outbound
This paper cites Dual Adaptive Transformations for Weakly Su- pervised Point Cloud Segmentation.ECCV, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Dual Adaptive Transformations for Weakly Su- pervised Point Cloud Segmentation.ECCV, 2022
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.974042Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 1ff59cf2-7820-404a-b941-8abb5354d113 · outbound
This paper cites 3D Open-V ocabulary Panoptic Segmentation with 2D-3D Vision-Language Distil- lation.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds 3D Open-V ocabulary Panoptic Segmentation with 2D-3D Vision-Language Distil- lation.ECCV, 2024
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.828289Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 9b8d70b9-5165-48b3-8020-8cd5d93dae80 · outbound
This paper cites PointContrast: Unsupervised Pre- training for 3D Point Cloud Understanding.ECCV, 2020.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PointContrast: Unsupervised Pre- training for 3D Point Cloud Understanding.ECCV, 2020
Reference 81
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.677115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 74d43d7b-3351-48d6-bd0d-cd226219070f · outbound
This paper cites PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Seg- mentation.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds PDF: A Probability-Driven Framework for Open World 3D Point Cloud Semantic Seg- mentation.CVPR, 2024
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.526979Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 495a17f0-3489-4b04-9299-0fdcb90a6811 · outbound
This paper cites Dual- level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation.ECCV, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Dual- level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation.ECCV, 2024
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.372749Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e9adb5d9-ca9b-4419-80e9-dae0d92d7b7e · outbound
This paper cites RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding.CVPR, 2024
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.262211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ed82435f-dcd9-4cbe-8987-f501b73c2c8b · outbound
This paper cites SAI3D: Segment Any Instance in 3D Scenes.CVPR, 2024.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds SAI3D: Segment Any Instance in 3D Scenes.CVPR, 2024
Reference 85
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:29:15.457522Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58a58e3b-4fee-42ec-b63c-79a1d6b4a864 · outbound
This paper cites TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmen- tation.ECCV, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds TransFGU: A Top-down Approach to Fine-Grained Unsupervised Semantic Segmen- tation.ECCV, 2022
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:19.111209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation eda187fb-29a1-4413-80a9-6bb4e9a31bcf · outbound
This paper cites Unsupervised Se- mantic Segmentation with Self-supervised Object-Centric Representations.ICLR, 2023.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised Se- mantic Segmentation with Self-supervised Object-Centric Representations.ICLR, 2023
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.973861Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b4bbde44-71b8-4189-b662-5244fc1ad057 · outbound
This paper cites Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Seg- mentation.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Perturbed Self-Distillation: Weakly Supervised Large-Scale Point Cloud Semantic Seg- mentation.ICCV, 2021
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.844174Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation e85a22bc-bde2-473b-8254-53251cc8cba7 · outbound
This paper cites Self-Supervised Pretraining of 3D Features on any Point-Cloud.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Self-Supervised Pretraining of 3D Features on any Point-Cloud.ICCV, 2021
Reference 89
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.706239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 0577967a-5f24-4976-92b0-fa1251c3c141 · outbound
This paper cites Self-Supervised Pre- training for Large-Scale Point Clouds.NeurIPS, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Self-Supervised Pre- training for Large-Scale Point Clouds.NeurIPS, 2022
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.503241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 43978568-176b-495a-944a-16ccfa8249c4 · outbound
This paper cites GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.378994Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2012cb20-6c18-4208-abd6-ec8187641f08 · outbound
This paper cites Unsupervised seepage segmen- tation pipeline based on point cloud projection with large vi- sion model.Tunnelling and Underground Space Technology,.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Unsupervised seepage segmen- tation pipeline based on point cloud projection with large vi- sion model.Tunnelling and Underground Space Technology,
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.237270Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 4a9dbf09-f3c4-4ff5-91b1-727130eb767d · outbound
This paper cites Point Transformer.ICCV, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Point Transformer.ICCV, 2021
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:18.025613Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation ac66c18b-4b94-4382-808d-419e966094b5 · outbound
This paper cites Extract Free Dense Labels from CLIP.ECCV, 2022.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Extract Free Dense Labels from CLIP.ECCV, 2022
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T05:29:17.861264Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b3767287-b225-42dd-a4d2-e64dcf835e60 · outbound
This paper cites Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Seg- mentation.CVPR, 2021.
LogoSP: Local-global Grouping of Superpoints for Unsupervised Semantic Segmentation of 3D Point Clouds Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Seg- mentation.CVPR, 2021
Reference 95
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T05:29:17.700884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Pith citing papers
No inbound Pith citation observations are available.