Paper Citation Record · LEDGER

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications

As of 10 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2501.17076.

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

pith.paper-citation-record.v1
2501.17076 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:53:01.829748Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy14
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87f4ea34-8615-465d-a19c-3d8d79a50c58 · outbound

This paper cites Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Cyber Mobility Mirror: A Deep Learning-based Real-World Object Perception Platform Using Roadside LiDAR

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.738022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a577ddf9-c5a6-479f-af06-409c10f333ff · outbound

This paper cites Apollo: A dataset profiling and operator modeling system.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Apollo: A dataset profiling and operator modeling system

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.210247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8e2d4c29-7e5e-4ae7-aa3b-b1c56a8d9a48 · outbound

This paper cites Unsupervised lidar-based 3d object detection using infrastructure sensors, 2022.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Unsupervised lidar-based 3d object detection using infrastructure sensors, 2022

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.188043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ef3d1688-3798-4cfe-b965-f18bad00354b · outbound

This paper cites Lumpi: The leibniz university multi-perspective intersection dataset.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Lumpi: The leibniz university multi-perspective intersection dataset

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.174645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 62268de5-79c0-44f2-934c-5a04ae7de52b · outbound

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

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications nuscenes: A multimodal dataset for autonomous driving

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.754081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02b586fc-ec67-4074-aa7a-d9efefda49c0 · outbound

This paper cites Big data deep learning: challenges and perspectives.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Big data deep learning: challenges and perspectives

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.154212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0927e8aa-38fe-4908-a5b5-e8f7fc12bb11 · outbound

This paper cites A9-dataset: Multi-sensor infrastructure-based dataset for mobility research.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications A9-dataset: Multi-sensor infrastructure-based dataset for mobility research

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.141398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 67154415-edcf-4dda-b210-e03d5feaf7b6 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications A density-based algorithm for discovering clusters in large spatial databases with noise

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.765153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25f5f6f2-a88a-4662-aad6-9ce13a4eacad · outbound

This paper cites Vision meets robotics: The kitti dataset.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Vision meets robotics: The kitti dataset

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.768612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 463daacd-fe9a-4d3b-b407-a02db044ddd7 · outbound

This paper cites Deep learning for 3d point clouds: A survey.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Deep learning for 3d point clouds: A survey

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.109123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5f8212a5-5616-47db-8668-b9bf7d91130d · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Revisiting unreasonable effectiveness of data in deep learning era

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.095236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 57b7afe2-7b5f-4f53-8f1d-38dd0cd4dd4a · outbound

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

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Scalability in perception for autonomous driving: Waymo open dataset

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.779233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7533868e-d69b-4316-9eb1-07c67c924d6e · outbound

This paper cites Ob- ject detection based on roadside lidar for cooperative driving automation: a review.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Ob- ject detection based on roadside lidar for cooperative driving automation: a review

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:02.072341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c754e4aa-8899-475d-a6c0-8905d5139f2d · outbound

This paper cites Unbiased look at dataset bias.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Unbiased look at dataset bias

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.787122Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a8fde3-c7fd-4c3b-81ff-9f609d2674fe · outbound

This paper cites an unresolved cited work.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:53:02.026283Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d0989122-70ef-458f-9bee-43dcc29f65c3 · outbound

This paper cites Ips300+: a challenging multi-modal data sets for intersection perception system.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Ips300+: a challenging multi-modal data sets for intersection perception system

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.996493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dce2b2f9-81e8-4f04-b077-f17226ce54e8 · outbound

This paper cites Automatic background filtering method for roadside lidar data.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Automatic background filtering method for roadside lidar data

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.983720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 71854daa-c38e-4ac4-ae87-f055aceab57e · outbound

This paper cites Pandaset: Advanced sensor suite dataset for autonomous driving.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Pandaset: Advanced sensor suite dataset for autonomous driving

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.970104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b4a8d5bc-2096-4402-88a6-d5a918b3bb26 · outbound

This paper cites Rope3d: the roadside perception dataset for autonomous driving and monocular 3d object detection task.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Rope3d: the roadside perception dataset for autonomous driving and monocular 3d object detection task

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.956928Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6fe3d31a-5429-48bf-a5de-0b18c5df4f93 · outbound

This paper cites BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications BAAI-VANJEE Roadside Dataset: Towards the Connected Automated Vehicle Highway technologies in Challenging Environments of China

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.810081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35771582-0fac-4b38-9b00-122c5a3a5104 · outbound

This paper cites Dair-v2x: A large- scale dataset for vehicle-infrastructure cooperative 3d object detection.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Dair-v2x: A large- scale dataset for vehicle-infrastructure cooperative 3d object detection

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.943745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fd80bad5-8656-4f5f-8909-a89a0c7373ef · outbound

This paper cites Zhang, W.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Zhang, W

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:53:01.929923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1ab6fafb-2517-4395-8e02-531ba80c8cd0 · outbound

This paper cites an unresolved cited work.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:53:01.913524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7e5efc19-8c55-42fa-92c6-177c512a7ecf · outbound

This paper cites an unresolved cited work.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-10T04:53:01.900176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c58ddf28-e261-4002-8c66-f8bba0d3c6ed · outbound

This paper cites Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation.

DINOSTAR: Deep Iterative Neural Object Detector Self-Supervised Training for Roadside LiDAR Applications Real-Time and Robust 3D Object Detection Within Road-Side LiDARs Using Domain Adaptation

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T04:53:01.829748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.