Paper Citation Record · LEDGER

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans

As of 17 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2504.21602.

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

pith.paper-citation-record.v1
2504.21602 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:04:18.597759Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-17T05:29:34.558894Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T05:31:33.707656Z

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy23
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d66d555c-3315-4db0-a682-9167c0ffd07a · outbound

This paper cites SemanticKITTI: A Dataset for Semantic Scene Under- standing of LiDAR Sequences,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans SemanticKITTI: A Dataset for Semantic Scene Under- standing of LiDAR Sequences,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:19.021891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0d097258-7ef2-4b91-9e54-cf92237e1534 · outbound

This paper cites Semanticposs: A point cloud dataset with large quantity of dynamic instances,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Semanticposs: A point cloud dataset with large quantity of dynamic instances,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:19.007914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6cb5bc7c-911b-445c-94e0-d4ce3b1d7e52 · outbound

This paper cites Lidarnet: A boundary-aware domain adapta- tion model for lidar point cloud semantic,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Lidarnet: A boundary-aware domain adapta- tion model for lidar point cloud semantic,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.993367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c207c1c6-0159-43d5-b2a6-5ba7d2b1e706 · outbound

This paper cites Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-16T05:04:18.493382Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d0de7a0-2632-4349-8271-98e129a6c63d · outbound

This paper cites Scalability in perception for autonomous driving: Waymo open dataset,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Scalability in perception for autonomous driving: Waymo open dataset,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.979752Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cf23dd22-9bb5-49d0-a364-bef3bdc7edd5 · outbound

This paper cites 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans 3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:04:18.695556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a393bcb3-15e2-4e54-b7a7-bea7c72face1 · outbound

This paper cites FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-16T05:04:18.508547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43b29b48-3c5a-4fb8-bfa6-09e30dea647c · outbound

This paper cites Fidnet: Lidar point cloud seman- tic segmentation with fully interpolation decoding,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Fidnet: Lidar point cloud seman- tic segmentation with fully interpolation decoding,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.966720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c4a0384e-7048-4660-888e-52625ca181ca · outbound

This paper cites Cenet: Toward concise and efficient lidar semantic segmentation for autonomous driving,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Cenet: Toward concise and efficient lidar semantic segmentation for autonomous driving,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.954298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0a49396f-f794-4d2c-8f08-f9daf7e069a5 · outbound

This paper cites Rethinking range view representation for lidar segmentation,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Rethinking range view representation for lidar segmentation,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.942235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e956b5da-d67c-4ccc-847f-b09e0ae9b89d · outbound

This paper cites Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.929964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 91f0b830-a42c-49a9-9655-268d8fd32830 · outbound

This paper cites Spherical transformer for lidar-based 3d recognition,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Spherical transformer for lidar-based 3d recognition,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.916697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4783a208-dd6b-4e9e-8681-0d0d98e35e65 · outbound

This paper cites Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T05:04:18.534934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5b73a06-b008-497d-889c-390dde8672a7 · outbound

This paper cites Search- ing efficient 3d architectures with sparse point-voxel convolution,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Search- ing efficient 3d architectures with sparse point-voxel convolution,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.903357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b801e3c6-c5f1-4d54-ac00-bd5578beb0b2 · outbound

This paper cites Point transformer v3: Simpler, faster, stronger,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Point transformer v3: Simpler, faster, stronger,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.889513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12273bac-9a1f-48a1-941c-52d5aea8c134 · outbound

This paper cites Using a waffle iron for automotive point cloud semantic segmentation,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Using a waffle iron for automotive point cloud semantic segmentation,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.876715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c9a085f8-90f2-45b3-b9e2-d68647b28092 · outbound

This paper cites Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.863251Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b1c9724c-efcf-43d8-b85b-ea5c2bc685e7 · outbound

This paper cites Point-to-voxel knowledge distillation for lidar semantic segmentation,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Point-to-voxel knowledge distillation for lidar semantic segmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.849507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 963b1366-39de-4e99-af2f-704a57b9fbea · outbound

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

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.836612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d32692ce-4490-47e0-90a5-5141198eef52 · outbound

This paper cites Sensor equivariance by lidar projection images,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Sensor equivariance by lidar projection images,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.823321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation caa5f19b-9685-4135-9ce0-e0f2a388f7d6 · outbound

This paper cites Height change feature based free space detection,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Height change feature based free space detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.809239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc200edb-7dbd-43fa-8357-2350c2e52e0f · outbound

This paper cites Deep residual learning for image recognition,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Deep residual learning for image recognition,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.795293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 61b4dea2-c910-43ae-a99f-9ce7445308f0 · outbound

This paper cites Shufflenet: An extremely efficient convolutional neural network for mobile devices,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Shufflenet: An extremely efficient convolutional neural network for mobile devices,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.780761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7ef30df8-f5e5-4ae4-ac5d-5606f8dd0938 · outbound

This paper cites Torchvision: Pytorch’s computer vision library.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Torchvision: Pytorch’s computer vision library

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.767177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eaa052d4-ce0d-4ee5-87b3-94a89e276f46 · outbound

This paper cites 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.753540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c133d0fa-a7dc-4c27-b22a-488ab9ddbc71 · outbound

This paper cites Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T05:04:18.585280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e67bf0a3-db15-449e-965e-5b26bb8f832d · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Adam: A Method for Stochastic Optimization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:04:18.589175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa652f8c-1f26-44da-873b-cd932646baba · outbound

This paper cites Robotic operating system.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Robotic operating system

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.738844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eb9b0445-265b-4c20-94fe-71e5ab989ed3 · outbound

This paper cites Clustering is back: Reaching state-of-the-art LiDAR instance segmentation without training,.

Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans Clustering is back: Reaching state-of-the-art LiDAR instance segmentation without training,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:04:18.725355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

Observation 8b2c6904-697d-4c39-a8c5-393929067b09 · inbound

Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds cites this paper.

Redefining Radar Segmentation: Simultaneous Static-Moving Segmentation and Ego-Motion Estimation using Radar Point Clouds Real Time Semantic Segmentation of High Resolution Automotive LiDAR Scans

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:31:33.711656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.