Paper Citation Record · LEDGER

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions

As of 22 July 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2604.10213.

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

pith.paper-citation-record.v1
2604.10213 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:47:33.123627Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache

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

23 of 23 outbound references displayed

verified exact6
verified fuzzy17
unresolved0
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7ce1cc62-ecbd-4877-ae50-c1278ffb441d · outbound

This paper cites PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.729885Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 71d7a0df-4555-46b7-b601-624593628c21 · outbound

This paper cites Toward physics-aware deep learning architectures for lidar intensity simulation.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Toward physics-aware deep learning architectures for lidar intensity simulation

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.740628Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation c37d4ede-844f-4514-88f6-2620d38477bd · outbound

This paper cites Realistic lidar data simulation for autonomous systems using physics-informed learning.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Realistic lidar data simulation for autonomous systems using physics-informed learning

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.705146Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 62b3c62c-79b8-4b7d-b48e-ca6deb2d811c · outbound

This paper cites Lidar snowfall simulation for robust 3d object detection.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Lidar snowfall simulation for robust 3d object detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.694281Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 310ca8ec-af8c-4204-b976-c47249d27c9a · outbound

This paper cites Learning to pre- dict lidar intensities.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Learning to pre- dict lidar intensities

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.711774Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 9d06a5fa-9c36-4088-b26e-e99506f5c43a · outbound

This paper cites Advancing lidar inten- sity simulation through learning with novel physics-based modalities.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Advancing lidar inten- sity simulation through learning with novel physics-based modalities

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.688357Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation dfff3192-8ac6-4908-ac99-73004655fe86 · outbound

This paper cites SimDaaS Simulator.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions SimDaaS Simulator

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.708384Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation d88d5c30-4f56-4d1c-8889-0a45c0b1e5e9 · outbound

This paper cites Lidarsim: Realistic lidar simu- lation by leveraging the real world.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Lidarsim: Realistic lidar simu- lation by leveraging the real world

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.685484Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 4fbc27a6-a2d0-4abc-bd1e-401978c3727d · outbound

This paper cites Learning to simulate realistic lidars.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Learning to simulate realistic lidars

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.691233Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation bf1b544f-0b44-455a-ae64-ac126cf2dd61 · outbound

This paper cites Review of the learning- based camera and lidar simulation methods for autonomous driving systems.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Review of the learning- based camera and lidar simulation methods for autonomous driving systems

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.750698Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation d0e939f3-411f-4bec-a310-85016ccdadde · outbound

This paper cites Seeing through fog without seeing fog: Deep multimodal sensor fusion in adverse weather.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Seeing through fog without seeing fog: Deep multimodal sensor fusion in adverse weather

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.715546Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 7d85528a-956e-40e3-95cb-9b217fe94470 · outbound

This paper cites Fog simulation on real lidar point clouds for 3d object detection in adverse weather.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Fog simulation on real lidar point clouds for 3d object detection in adverse weather

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.722759Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b75ebda2-22e5-4fcf-9d24-d95e02ac7b9c · outbound

This paper cites Lidardiffusion: Learning realistic lidar simulation via diffusion models.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Lidardiffusion: Learning realistic lidar simulation via diffusion models

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.719222Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 25f23e41-f2f5-4528-a446-5f955b64086f · outbound

This paper cites Semantickitti: A dataset for semantic scene understanding of lidar sequences.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Semantickitti: A dataset for semantic scene understanding of lidar sequences

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.697542Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 7196883e-5b66-4c45-8088-e73eda83fab4 · outbound

This paper cites nuscenes: A multi- modal dataset for autonomous driving.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions nuscenes: A multi- modal dataset for autonomous driving

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.701889Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 9dd974fb-7720-49ff-9b89-6893319e81fd · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Are we ready for autonomous driving? the kitti vision benchmark suite

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.739670Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation dfd8e557-ba9c-4105-a723-a7bfb27f271a · outbound

This paper cites Canadian Adverse Driving Conditions Dataset.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Canadian Adverse Driving Conditions Dataset

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.720720Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation a60bc927-3405-4eb5-8e9a-6117f05e0d91 · outbound

This paper cites Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.709591Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 00b6d8b5-312f-42b7-ac6a-e9aae398c687 · outbound

This paper cites Towards realistic lidar intensity simulation in snowy weather using physics-informed learning.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Towards realistic lidar intensity simulation in snowy weather using physics-informed learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.735609Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 90a55ceb-a7e7-42f0-804a-eb1e6f06d524 · outbound

This paper cites Toward closing the sim-to-real gap: A physics-guided learning approach for lidar intensity simulation.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Toward closing the sim-to-real gap: A physics-guided learning approach for lidar intensity simulation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.731127Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 07f66591-8827-4ea6-a9d1-f8314aab5e44 · outbound

This paper cites Simulating realistic lidar data under adverse weather for autonomous vehicles: A physics- informed learning approach.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Simulating realistic lidar data under adverse weather for autonomous vehicles: A physics- informed learning approach

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:50:59.715975Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation b377b202-d90a-4a3b-abea-f4b6ff1521e4 · outbound

This paper cites Pv- rcnn: Point-voxel feature set abstraction for 3d object detection.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Pv- rcnn: Point-voxel feature set abstraction for 3d object detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.726861Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 9603eae5-b571-4823-bbb4-736294297f49 · outbound

This paper cites Openpcdet: An open-source toolbox for 3d object detection from point clouds.

ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions Openpcdet: An open-source toolbox for 3d object detection from point clouds

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-17T18:45:05.743612Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Pith citing papers

No inbound Pith citation observations are available.