Paper Citation Record · LEDGER

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments

As of 18 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2505.21914.

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

pith.paper-citation-record.v1
2505.21914 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:23:42.328990Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

22 of 22 outbound references displayed

verified exact1
verified fuzzy16
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a5b5d8ba-7247-446e-a1cf-1174d5372619 · outbound

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

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:40.519425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06804db4-d20f-41fd-a046-55f73bca279c · outbound

This paper cites nuscenes: A multimodal dataset for autonomous driving,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments nuscenes: A multimodal dataset for autonomous driving,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:40.577668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34a41062-a011-404a-aae6-3e19a04801c2 · outbound

This paper cites Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Large scale interactive motion forecasting for autonomous driving: The waymo open motion dataset,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:45.055338Z

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 27eedd3e-4fa9-472b-a125-a0e1bd57c814 · outbound

This paper cites Argoverse: 3d tracking and forecasting with rich maps,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Argoverse: 3d tracking and forecasting with rich maps,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.952420Z

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 c8d2c6df-046e-430f-89fa-90f0d61280ed · outbound

This paper cites A* 3d dataset: Towards autonomous driving in challenging environments,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments A* 3d dataset: Towards autonomous driving in challenging environments,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.828827Z

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 0209d004-2b32-4ad7-910f-65c6437f57b1 · outbound

This paper cites A2D2: Audi Autonomous Driving Dataset.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments A2D2: Audi Autonomous Driving Dataset

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:40.953325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17599165-8bfc-4758-ba10-4530ffde06c2 · outbound

This paper cites One Million Scenes for Autonomous Driving: ONCE Dataset.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments One Million Scenes for Autonomous Driving: ONCE Dataset

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:41.104120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02aaee95-e499-4354-adae-d53c057b37f7 · outbound

This paper cites Semantickitti: A dataset for semantic scene un- derstanding of lidar sequences,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Semantickitti: A dataset for semantic scene un- derstanding of lidar sequences,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.536809Z

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 62a07b21-b089-4478-8738-80c8d05369e0 · outbound

This paper cites Automine: An unmanned mine dataset,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Automine: An unmanned mine dataset,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.394017Z

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 8ee572d6-8fa8-423f-b6c1-a1becabdf7e4 · outbound

This paper cites Pointpillars: Fast encoders for object detection from point clouds,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Pointpillars: Fast encoders for object detection from point clouds,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.228524Z

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 db3a0c65-7d43-46b6-aac5-892422efe550 · outbound

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

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Pv-rcnn: Point-voxel feature set abstraction for 3d object detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:44.049319Z

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 c5e0e377-e0f0-429c-a789-531833da525b · outbound

This paper cites Centerpoints: A link between optimization and convex geometry,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Centerpoints: A link between optimization and convex geometry,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.917210Z

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 d28f50ab-77e3-49ee-82be-4698a0649969 · outbound

This paper cites Pillarnet: Real-time and high-performance pillar-based 3d object detection,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Pillarnet: Real-time and high-performance pillar-based 3d object detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.806304Z

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 43bd833b-bee0-4c57-984d-a1d922bb4ed7 · outbound

This paper cites V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.668667Z

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 ea8d2c9b-bcb9-47c5-8ae3-15f4a3b4b406 · outbound

This paper cites Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.542070Z

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 72df9548-9342-48ce-b997-a09fe9777125 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.402312Z

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 025521de-e59c-4eb3-99fb-945f61cc294e · outbound

This paper cites Randla-net: Efficient semantic segmentation of large-scale point clouds,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Randla-net: Efficient semantic segmentation of large-scale point clouds,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.249621Z

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 2c002ef7-8d48-4c43-bca6-82960e99591c · outbound

This paper cites Cenet: Consolidation-and-exploration network for continuous domain adapta- tion,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Cenet: Consolidation-and-exploration network for continuous domain adapta- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:43.132248Z

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 04e8817f-2357-4eaa-9260-46db393026e2 · outbound

This paper cites Cylin- der3d: An effective 3d framework for driving-scene lidar semantic seg- mentation.,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments Cylin- der3d: An effective 3d framework for driving-scene lidar semantic seg- mentation.,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:42.986939Z

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 3808739b-aa94-4a88-ac41-6d4992861226 · outbound

This paper cites LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments LiSD: An Efficient Multi-Task Learning Framework for LiDAR Segmentation and Detection

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-07T13:23:42.571632Z

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 e934a3d7-276d-452c-bfd3-aa50249beef9 · outbound

This paper cites The pascal visual object classes (voc) challenge,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments The pascal visual object classes (voc) challenge,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:42.242673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbe6bf5a-7877-412d-b46d-77e5a5894130 · outbound

This paper cites V oxelnet: End-to-end learning for point cloud based 3d object detection,.

LiDARDustX: A LiDAR Dataset for Dusty Unstructured Road Environments V oxelnet: End-to-end learning for point cloud based 3d object detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:23:42.829129Z

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.

Pith citing papers

No inbound Pith citation observations are available.