Paper Citation Record · LEDGER

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments

As of 7 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.22258.

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

pith.paper-citation-record.v1
2505.22258 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:14:40.943800Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

33 of 33 outbound references displayed

verified exact1
verified fuzzy26
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5f634aac-19f6-4b6e-9ce4-b9b32cd862e2 · outbound

This paper cites 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation.IEEE Robotics and Automation Letters, 5:5432–5439, 2020.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments 3d-mininet: Learning a 2d representation from point clouds for fast and efficient 3d lidar semantic segmentation.IEEE Robotics and Automation Letters, 5:5432–5439, 2020

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:46.157963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48db68a9-d939-4733-a005-f71ab6c9911d · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Rangevit: Towards vision transformers for 3d semantic segmentation in autonomous driving

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.879134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ccb836e5-ed1f-419c-9e63-efc9e5d38641 · outbound

This paper cites Efficient human 3d localization and free space segmentation for human-aware mobile robots in warehouse facilities.Frontiers in Robotics and AI, 10:1283322, 10 2023.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Efficient human 3d localization and free space segmentation for human-aware mobile robots in warehouse facilities.Frontiers in Robotics and AI, 10:1283322, 10 2023

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.730358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8b8058a-3cc0-4739-8995-cfcea61f827c · outbound

This paper cites Behley, M.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Behley, M

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.566310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4936764c-50b4-4318-aa2c-1ea76b5d0d08 · outbound

This paper cites Cenet: Toward concise and efficient lidar semantic segmentation for autonomous driv- ing.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Cenet: Toward concise and efficient lidar semantic segmentation for autonomous driv- ing

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.425813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 029065da-ff36-4b8e-9b5a-23e671c60d32 · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:38.360907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c57cce68-6c6e-4d01-ae4f-5bdddd2acf32 · outbound

This paper cites Excavating in the wild: The goose-ex dataset for semantic segmentation.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Excavating in the wild: The goose-ex dataset for semantic segmentation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.263400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa0e2c7b-c0d8-4f08-9ea2-09a7de259f89 · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Asymmetric Loss Functions and Deep Densely Connected Networks for Highly Imbalanced Medical Image Segmentation: Application to Multiple Sclerosis Lesion Detection

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:14:41.140248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation af3737c6-ab94-42ca-ab96-0b1375e6bee5 · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Zhang, Shaoqing Ren, and Jian Sun

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:45.079993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3873c130-3e73-485e-a94f-f90ad12ae4dc · outbound

This paper cites ISO 8855:2011 Road vehicles — Vehicle dynamics and road-holding ability — V ocabulary.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments ISO 8855:2011 Road vehicles — Vehicle dynamics and road-holding ability — V ocabulary

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:44.877576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2e2bed68-900c-4806-8fef-9a10f9c66109 · outbound

This paper cites Lidarnet: A boundary-aware domain adaptation model for lidar point cloud semantic, 2020.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Lidarnet: A boundary-aware domain adaptation model for lidar point cloud semantic, 2020

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:44.697416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0f024f81-cfd0-4a8c-a6fc-a52150f992ec · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Adam: A Method for Stochastic Optimization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:39.131408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90b36b27-b655-4167-99f2-c38ebe60a550 · outbound

This paper cites Rethinking range view representation for lidar segmentation.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Rethinking range view representation for lidar segmentation

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:44.542519Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a722527e-767b-4dae-afe1-a9bec9823867 · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Spherical transformer for lidar-based 3d recognition

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:44.400801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1b63fede-3e46-4723-bc78-1c36a876e767 · outbound

This paper cites Feature pyramid networks for object detection.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Feature pyramid networks for object detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:44.183043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8c6afe03-68d7-49ba-841c-e773a6546357 · outbound

This paper cites an unresolved cited work.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:14:43.997163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation efeebf45-86e2-4191-917a-151d3e5a4d08 · outbound

This paper cites Semanticposs: A point cloud dataset with large quantity of dynamic instances, 2020.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Semanticposs: A point cloud dataset with large quantity of dynamic instances, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:43.851163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1464bc40-1c18-4028-8d7c-ba4bee2f28c4 · outbound

This paper cites Sensor equivariance by lidar projection images.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Sensor equivariance by lidar projection images

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:43.679943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3f546949-e124-4fb3-9e59-d2e87b54f8fb · outbound

This paper cites Semanticthab: A high resolution lidar dataset, Feb.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Semanticthab: A high resolution lidar dataset, Feb

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:43.523570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4f8f03cf-410e-41ac-a50d-41f55d42cc0c · outbound

This paper cites Real time semantic segmentation of high resolution automotive lidar scans, 2025.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Real time semantic segmentation of high resolution automotive lidar scans, 2025

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:43.358270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46d4406a-5946-42db-9ec7-c5a61477f89f · outbound

This paper cites Height change feature based free space detection.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Height change feature based free space detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:43.115942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 39d93e33-84e1-4f16-a186-f9bb3e6040ee · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Scalability in perception for autonomous driving: Waymo open dataset

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.932566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5b36e867-d5f3-466a-9fd0-3f16abdc7f52 · outbound

This paper cites Efficientnetv2: Smaller models and faster training.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Efficientnetv2: Smaller models and faster training

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.762719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 978077f8-0e2a-4592-8f51-616f2cb61642 · outbound

This paper cites Searching efficient 3d architectures with sparse point-voxel convolution.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Searching efficient 3d architectures with sparse point-voxel convolution

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.629703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 232e181b-6678-4dd4-843f-cdfd41f3dc87 · outbound

This paper cites Attention is all you need.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Attention is all you need

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.510051Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1a5c1601-3b08-421e-9706-94554e1ea464 · outbound

This paper cites Vdbfusion: Flexible and efficient tsdf integration of range sensor data.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Vdbfusion: Flexible and efficient tsdf integration of range sensor data

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.332362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 00ff9f2e-99c4-4ea6-889a-bc85a9b7c336 · outbound

This paper cites KISS-ICP: In Defense of Point-to- Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way.IEEE Robotics and Automation Letters (RA-L), 8(2):1029–1036,.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments KISS-ICP: In Defense of Point-to- Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way.IEEE Robotics and Automation Letters (RA-L), 8(2):1029–1036,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:42.112954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 245b6d59-9db5-49d8-9af0-80e5f1119670 · outbound

This paper cites Sfpnet: Sparse focal point network for semantic segmentation on general lidar point clouds.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Sfpnet: Sparse focal point network for semantic segmentation on general lidar point clouds

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:41.918013Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f8927fd1-aea3-4102-bb1c-89c297d2c19a · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Point transformer v3: Simpler, faster, stronger

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:41.742078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1204b442-bed2-4898-8507-c80d5ebf42fd · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments FRNet: Frustum-Range Networks for Scalable LiDAR Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:40.682365Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c7f85c5-fd8b-492a-b609-d709ccf82d56 · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Shufflenet: An extremely efficient convolutional neural network for mobile devices

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:14:41.545185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0377501f-e603-4cb0-b769-987804a59bb0 · outbound

This paper cites an unresolved cited work.

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:14:41.275195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 92869673-745d-4f5b-aa7c-3c598d3568e7 · outbound

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

LiDAR Based Semantic Perception for Forklifts in Outdoor Environments Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:40.943800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.