Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:23:39.633484Z
Paper Citation Record · LEDGER
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark
As of 17 August 2026, this Paper Citation Record lists 52 of 52 outbound references and 0 inbound Pith citation observations for arXiv:2608.07757.
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 2026-08-17T06:30:58.91139+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
52 of 52 outbound references displayed
- verified exact3
- verified fuzzy40
- unresolved8
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation aef8490b-d525-45b2-a90c-e5de8f3644b7 · outbound
This paper cites Big Data in the construction industry: A review of present status, opportunities, and future trends,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Big Data in the construction industry: A review of present status, opportunities, and future trends,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:42.022619Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 174a918a-dfc9-4021-93b5-4e10731bfb2e · outbound
This paper cites Classical strategies include random sampling, farthest-point sampling, and voxel- or grid-based sampling, which remain widely used for their simplicity and efficiency.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Classical strategies include random sampling, farthest-point sampling, and voxel- or grid-based sampling, which remain widely used for their simplicity and efficiency
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:44.303003Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a16d04cf-7912-446b-8cb4-1c196af198be · outbound
This paper cites Geometry Intuition: Sensor-Centered Sampling Bias Single-scan LiDAR point clouds do not represent volumetric occupancy.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Geometry Intuition: Sensor-Centered Sampling Bias Single-scan LiDAR point clouds do not represent volumetric occupancy
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:44.032244Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fb1eb79d-9646-402f-a373-90456179d5a7 · outbound
This paper cites an unresolved cited work.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-11T00:23:43.716430Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 20ed18f5-cf46-4dc0-9ecc-6edff13e02d9 · outbound
This paper cites Design considerations The maximum number of points that can be processed in a scene fragment defines the primary computational constraint in large -scale 3D segmentation.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Design considerations The maximum number of points that can be processed in a scene fragment defines the primary computational constraint in large -scale 3D segmentation
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:43.363337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fd6bebc0-39f1-485d-a29e-bcfe0affc648 · outbound
This paper cites Reference comparison with FPS Figure 9 compares the retained-point distributions produced by grid sampling, farthest point sampling (FPS), manifold sampling, and manifold+.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Reference comparison with FPS Figure 9 compares the retained-point distributions produced by grid sampling, farthest point sampling (FPS), manifold sampling, and manifold+
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:43.074459Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 247938c5-dcde-4a69-84a4-b7577f66d0cb · outbound
This paper cites an unresolved cited work.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Unresolved cited work
Reference 7
- Resolution
- verified exact
- doi, observed 2026-08-11T00:23:42.578922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 9cea0a4f-9c03-435a-8893-13c170167155 · outbound
This paper cites BIM information integration based VR modeling in digital twins in industry 5.0,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark BIM information integration based VR modeling in digital twins in industry 5.0,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.413304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 95eb21a1-159c-4ab8-954f-16ef18254a28 · outbound
This paper cites Towards big data driven construction industry,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Towards big data driven construction industry,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.807874Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4913a4c1-decb-4486-9985-4de585b74fdc · outbound
This paper cites The future of construction automation: Technological disruption and the upcoming ubiquity of robotics,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark The future of construction automation: Technological disruption and the upcoming ubiquity of robotics,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.691905Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f8cb12f1-9e6a-4dba-9a60-893d9e4aa611 · outbound
This paper cites Future of robotics and automation in construction,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Future of robotics and automation in construction,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.512673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fdf35c8c-11af-485d-aad1-085475383b58 · outbound
This paper cites Review of image-based 3D reconstruction of building for automated construction progress monitoring,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Review of image-based 3D reconstruction of building for automated construction progress monitoring,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.466795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b331e1cb-dbfe-49c2-a008-5de67af66e5f · outbound
This paper cites Automated continuous construction progress monitoring using multiple workplace real time 3D scans,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Automated continuous construction progress monitoring using multiple workplace real time 3D scans,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.449304Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6f51ab9d-e598-4a5c-bbac-303470cb7f50 · outbound
This paper cites A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.429181Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a093aa86-af60-426e-8aa2-2be59af9433e · outbound
This paper cites An adaptive down-sampling method of laser scan data for scan-to-BIM,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark An adaptive down-sampling method of laser scan data for scan-to-BIM,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.311808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3590113e-1a17-4b59-a6f3-a99e03ff17a8 · outbound
This paper cites Construction quality assessment using 3D as-built models generated with Project Tango,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Construction quality assessment using 3D as-built models generated with Project Tango,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.395321Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 294e0d46-a5d0-4325-aefb-b13e442f16a1 · outbound
This paper cites Planning for terrestrial laser scanning in construction: A review,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Planning for terrestrial laser scanning in construction: A review,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.365658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 26ce9696-34e9-44ff-b7b8-59008768ad05 · outbound
This paper cites A review of point cloud segmentation for understanding 3D indoor scenes,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark A review of point cloud segmentation for understanding 3D indoor scenes,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.346537Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3a7ade8b-412e-4d53-9179-c67c7585b7d2 · outbound
This paper cites SIP: Site in Pieces- A Dataset of Disaggregated Construction-Phase 3D Scans for Semantic Segmentation and Scene Understanding.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark SIP: Site in Pieces- A Dataset of Disaggregated Construction-Phase 3D Scans for Semantic Segmentation and Scene Understanding
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T00:23:39.903075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c39e5afc-f6f6-4298-bf32-8f4ba841a1e8 · outbound
This paper cites Dynamic downsampling algorithm for 3D point cloud map based on voxel filtering,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Dynamic downsampling algorithm for 3D point cloud map based on voxel filtering,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.329648Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a9e37065-1762-4b61-8e04-4686caa094ae · outbound
This paper cites Scannet: Richly- annotated 3d reconstructions of indoor scenes,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Scannet: Richly- annotated 3d reconstructions of indoor scenes,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.924069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ad02b549-5c7d-4fec-bc8c-896e11a47e4a · outbound
This paper cites Adaptive hierarchical down-sampling for point cloud classification,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Adaptive hierarchical down-sampling for point cloud classification,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.296278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 91978f31-1918-4a75-a17f-d8e7350863e6 · outbound
This paper cites Deep learning for 3d point clouds: A survey,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Deep learning for 3d point clouds: A survey,
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.175676Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 88af6235-fae1-4d06-8e33-14f556df52bb · outbound
This paper cites Samplenet: Differentiable point cloud sampling,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Samplenet: Differentiable point cloud sampling,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.138993Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0deeb3e0-706c-420d-bc8a-6cc7878bf57a · outbound
This paper cites Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:41.019973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 22db60f4-a06f-4298-bef1-5fc74169818a · outbound
This paper cites Lsnet: Learned sampling network for 3d object detection from point clouds,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Lsnet: Learned sampling network for 3d object detection from point clouds,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.957832Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 19503ab7-0ced-4cc6-9b0a-94fdb7453c59 · outbound
This paper cites 3d semantic parsing of large-scale indoor spaces,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark 3d semantic parsing of large-scale indoor spaces,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.942096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 58124e54-2a21-4cc8-a3b1-052addb13b7d · outbound
This paper cites Scalability in perception for autonomous driving: Waymo open dataset,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Scalability in perception for autonomous driving: Waymo open dataset,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.814379Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 63c76cbc-141b-4bd7-9b80-bdd1709bf0d5 · outbound
This paper cites Scannet++: A high-fidelity dataset of 3d indoor scenes,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Scannet++: A high-fidelity dataset of 3d indoor scenes,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.895101Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 871e740d-6c83-4969-ac34-a6ae2957bb03 · outbound
This paper cites Matterport3D: Learning from RGB-D Data in Indoor Environments.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Matterport3D: Learning from RGB-D Data in Indoor Environments
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.234843Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b27b9c3d-7e3f-4d51-b19c-93ad4d2ece6d · outbound
This paper cites Structured3d: A large photo-realistic dataset for structured 3d modeling,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Structured3d: A large photo-realistic dataset for structured 3d modeling,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.880816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c0cf14d2-75be-4fb0-abb1-8eb47134c8c9 · outbound
This paper cites ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.253406Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 486d3e5b-20ae-434a-abd4-605af7cd7e6d · outbound
This paper cites Semantickitti: A dataset for semantic scene understanding of lidar sequences,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Semantickitti: A dataset for semantic scene understanding of lidar sequences,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.861068Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c94c4f48-b3a4-4ad7-9bb0-b4b6f086d7ca · outbound
This paper cites nuscenes: A multimodal dataset for autonomous driving,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark nuscenes: A multimodal dataset for autonomous driving,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.838557Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6b87291f-f577-4e27-998a-085be945d8c3 · outbound
This paper cites Stratified transformer for 3d point cloud segmentation,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Stratified transformer for 3d point cloud segmentation,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.336665Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7dd6e4da-dd38-4323-85ed-29c173ba3446 · outbound
This paper cites Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T00:23:39.813553Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a1406884-4f6b-47f0-989b-ca10f89b9f68 · outbound
This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark 4d spatio-temporal convnets: Minkowski convolutional neural networks,
Reference 38
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.318517Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8e2e6703-21a2-43ca-98e8-53fa0a383278 · outbound
This paper cites Searching efficient 3d architectures with sparse point-voxel convolution,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Searching efficient 3d architectures with sparse point-voxel convolution,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.785405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c39c4a4a-d7b8-4a3b-b0d2-607884b37a8b · outbound
This paper cites Point transformer,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Point transformer,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.768907Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 33648a83-6c7f-4783-955d-a54aabf89f4c · outbound
This paper cites Swin3d: A pretrained transformer backbone for 3d indoor scene understanding,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Swin3d: A pretrained transformer backbone for 3d indoor scene understanding,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.464995Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6a5e2665-fe4a-4cff-a6a3-f4ea698779e0 · outbound
This paper cites Pointnext: Revisiting pointnet++ with improved training and scaling strategies,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Pointnext: Revisiting pointnet++ with improved training and scaling strategies,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.256590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4b0ff297-2369-4019-a115-da75de21fb7b · outbound
This paper cites Sonata: Self-supervised learning of reliable point representations,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Sonata: Self-supervised learning of reliable point representations,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.323286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 67ed2dc2-28ec-4d15-8007-59ed5f048cc1 · outbound
This paper cites Concerto: Joint 2d-3d self-supervised learning emerges spatial representations,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Concerto: Joint 2d-3d self-supervised learning emerges spatial representations,
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.589331Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ec340010-f193-4783-bc12-45ec88bfe18a · outbound
This paper cites Together, the two backbones allow the sampling effect to be examined across different feature-aggregation mechanisms within the hierarchical point-based model family.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Together, the two backbones allow the sampling effect to be examined across different feature-aggregation mechanisms within the hierarchical point-based model family
Reference 46
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-11T00:23:43.252157Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c8fdf103-4e9b-48d2-bf30-f9695de97b2c · outbound
This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Point-bert: Pre-training 3d point cloud transformers with masked point modeling,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.307627Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d10df776-dc32-44fc-b852-a2175dc803c0 · outbound
This paper cites Utonia: Toward One Encoder for All Point Clouds.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Utonia: Toward One Encoder for All Point Clouds
Reference 48
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.600529Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1bb5c763-f22d-41a4-aaf8-894ebbe3d3f0 · outbound
This paper cites Towards large-scale 3d representation learning with multi-dataset point prompt training,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Towards large-scale 3d representation learning with multi-dataset point prompt training,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.291165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7b110278-03d2-42bc-b484-e7eaae5ce2c9 · outbound
This paper cites The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark The lovász-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks,
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.275118Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fa60e10c-620e-4d4f-94b3-e3f2efb214b1 · outbound
This paper cites Pointcept: A codebase for point cloud perception research.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Pointcept: A codebase for point cloud perception research
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.203179Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 60a4e182-2392-440f-92d5-7ed522fd3920 · outbound
This paper cites Point transformer v2: Grouped vector attention and partition-based pooling,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Point transformer v2: Grouped vector attention and partition-based pooling,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.611438Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0e1d862c-b586-42d3-809c-912926b0daef · outbound
This paper cites Point transformer v3: Simpler faster stronger,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark Point transformer v3: Simpler faster stronger,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-11T00:23:40.100073Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 35b95d56-ffa3-4a17-a7de-1701a0485d28 · outbound
This paper cites 3d semantic segmentation with submanifold sparse convolutional networks,.
Rethinking 3D Segmentation from Individual LiDAR Scans: Incidence-Aware Sampling on the SIP Benchmark 3d semantic segmentation with submanifold sparse convolutional networks,
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:23:39.633484Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.