Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2003.03653.
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 2026-08-12T06:34:41.77262+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T00:21:15.561750Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T13:46:58.812876Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 48296489-b7da-4b17-8f26-8adc150991c3 · inbound
LeAP: Consistent multi-domain 3D labeling using Foundation Models cites this paper.
LeAP: Consistent multi-domain 3D labeling using Foundation Models SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T00:21:15.561750Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 45e0b179-4c62-472f-a7f9-78aa45fd6e38 · inbound
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments cites this paper.
Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:50:30.502221Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9471a12b-3696-4b45-a1d8-66056697b4f4 · inbound
Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints cites this paper.
Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T07:48:20.599613Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 881b1bb5-fd06-45e8-ab5a-f8b95dd6149d · inbound
Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization cites this paper.
Privacy-Preserving Depth-Only Open-Vocabulary 3D Semantic Segmentation Via Uncertainty-Guided Test-Time Optimization SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T13:46:58.814442Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.
Observation 19640423-0ea8-4808-a958-978b3883a40d · inbound
Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression cites this paper.
Proteus: A Truncation-Robust Entropy Model for Progressive LiDAR Compression SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T00:38:19.697910Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.