Paper Citation Record · LEDGER

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising

As of 20 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2507.06976.

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

pith.paper-citation-record.v1
2507.06976 v1

Coverage vector

measured 32 of 32 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-06T18:57:32.112182Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

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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

32 of 32 outbound references displayed

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External citation measurements

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Outbound references

Observation 3f55d941-49e1-476a-b0bf-5595b4debf64 · outbound

This paper cites Road traffic injuries,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Road traffic injuries,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.773730Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2fd704bc-1a44-40c0-aebd-9ba171c9c75a · outbound

This paper cites Enhancing robustness of LiDAR-Based perception in adverse weather using point cloud augmentations,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Enhancing robustness of LiDAR-Based perception in adverse weather using point cloud augmentations,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.753305Z

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Observation 4ed196f8-3436-46e5-b182-bcb487919b61 · outbound

This paper cites Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Towards Robust CNN-based Object Detection through Augmentation with Synthetic Rain Variations,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.736951Z

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Observation b2f9f70e-a88a-45af-a2b0-91dda874c477 · outbound

This paper cites Mr3d-net: Dynamic multi-resolution 3d sparse voxel grid fusion for lidar-based collective perception,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Mr3d-net: Dynamic multi-resolution 3d sparse voxel grid fusion for lidar-based collective perception,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.719463Z

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Observation 3e0399b7-0a5d-44c0-a600-e68a3a8dec93 · outbound

This paper cites Simulating realistic rain, snow, and fog variations for comprehensive performance characterization of lidar perception,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Simulating realistic rain, snow, and fog variations for comprehensive performance characterization of lidar perception,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.699984Z

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Observation 6e659b0e-4075-4ffb-849f-64a69841dd21 · outbound

This paper cites Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Cooper: Cooperative perception for connected autonomous vehicles based on 3d point clouds,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.683880Z

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Observation 65d0718d-9523-4214-877c-c931eb23ecf2 · outbound

This paper cites OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising OPV2V: An open benchmark dataset and fusion pipeline for perception with vehicle-to- vehicle communication,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.666875Z

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Observation 12c758c3-0911-4c35-9462-81641146821d · outbound

This paper cites F-Cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising F-Cooper: Feature based cooperative perception for autonomous vehicle edge computing system using 3d point clouds,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.650337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc959a0c-a1db-4d49-a2ef-3ee52e6d5106 · outbound

This paper cites Pillargrid: Deep learning-based cooperative perception for 3d object detection from onboard-roadside lidar,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Pillargrid: Deep learning-based cooperative perception for 3d object detection from onboard-roadside lidar,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.634212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bc53b37a-448f-4445-9783-e20dc0c666b6 · outbound

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

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Pointpillars: Fast encoders for object detection from point clouds,

Reference 10

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Source-reported events for the cited work

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Observation 3a0e5949-9c63-4f79-93fe-10d28f3817c5 · outbound

This paper cites Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Keypoints-based deep feature fusion for cooperative vehicle detection of autonomous driving,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.606886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 315ea34d-4ef4-4bed-a80d-30c64a27d8a4 · outbound

This paper cites Bridging the domain gap for multi-agent perception,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Bridging the domain gap for multi-agent perception,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.592134Z

Source-reported events for the cited work

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Observation f29ad94e-5457-4374-9d27-9b9460e82a43 · outbound

This paper cites Weighted boxes fusion: Ensembling boxes from different object detection models,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Weighted boxes fusion: Ensembling boxes from different object detection models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.576685Z

Source-reported events for the cited work

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Observation ab930185-270c-4f12-b288-19cec7feb4ab · outbound

This paper cites A track-to- track association method for automotive perception systems,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising A track-to- track association method for automotive perception systems,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.560736Z

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Observation 4014fe3f-01d2-4baf-a493-f419fcdc686e · outbound

This paper cites A generic video and radar data fusion system for improved target selection,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising A generic video and radar data fusion system for improved target selection,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.545990Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0430bfe3-a57a-4b5f-b27b-7df58df0fcb5 · outbound

This paper cites Aeberhard, Object-level Fusion for Surround Environment Percep- tion in Automated Driving Applications , ser.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Aeberhard, Object-level Fusion for Surround Environment Percep- tion in Automated Driving Applications , ser

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.530650Z

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Observation 2db374c3-ab7f-4a38-b2a9-a68e2908aef0 · outbound

This paper cites Heterogeneous track-to-track fusion using equivalent measurement and unscented transform,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Heterogeneous track-to-track fusion using equivalent measurement and unscented transform,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.515719Z

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Observation d583b4c3-2e19-4ec5-8772-8895a88bc0dc · outbound

This paper cites Environment-aware Development of Robust Vision-based Cooperative Perception Systems,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Environment-aware Development of Robust Vision-based Cooperative Perception Systems,

Reference 18

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Observation 18dbee3c-aea3-4607-ad93-6548d958459d · outbound

This paper cites Infrastructure- supported perception and track-level fusion using edge computing,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Infrastructure- supported perception and track-level fusion using edge computing,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.481754Z

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Observation a577b835-890c-4af0-84ee-59febd0bdd9f · outbound

This paper cites Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Towards Realistic Evaluation of Collective Perception for Connected and Automated Driving,

Reference 20

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Observation c8492abb-80e8-40da-9b3b-cc5a4205006e · outbound

This paper cites Collective pv-rcnn: A novel fusion technique using collective detections for enhanced local lidar-based perception,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Collective pv-rcnn: A novel fusion technique using collective detections for enhanced local lidar-based perception,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.446845Z

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Observation 86c44075-edea-479f-a0bd-2cfc7d85d01c · outbound

This paper cites Collective perception datasets for autonomous driving: A comprehen- sive review,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Collective perception datasets for autonomous driving: A comprehen- sive review,

Reference 22

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Observation 02f345b9-b4d7-483a-9437-b9354f1db538 · outbound

This paper cites Weather-aware collaborative perception with uncertainty reduction,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Weather-aware collaborative perception with uncertainty reduction,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.410533Z

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Observation 83e6a245-4240-46a8-8984-7d21eeebcac0 · outbound

This paper cites V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection

Reference 24

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Source-reported events for the cited work

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Observation 48dff86e-8eaa-46ad-81e7-4429bf947f42 · outbound

This paper cites The lognormal fit to raindrop spectra from frontal convective clouds in israel,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising The lognormal fit to raindrop spectra from frontal convective clouds in israel,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.393713Z

Source-reported events for the cited work

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Observation 01329838-3bad-484c-9d61-7d2eb477ca77 · outbound

This paper cites The distribution with size of aggregate snowflakes,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising The distribution with size of aggregate snowflakes,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.375081Z

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Observation 9fd2f33b-52d1-47ff-91bf-9375d0f6c7d5 · outbound

This paper cites Snow size spectra and radar reflectivity,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Snow size spectra and radar reflectivity,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.358718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ca49afe2-35c8-4579-973d-f5592d765b9b · outbound

This paper cites Deep learning for lidar point clouds in autonomous driving: A review,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Deep learning for lidar point clouds in autonomous driving: A review,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.342850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2309d039-74d6-45d8-b104-7df5a24d8d2a · outbound

This paper cites S2s-net: Addressing the domain gap of heterogeneous sensor systems in lidar-based collective perception,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising S2s-net: Addressing the domain gap of heterogeneous sensor systems in lidar-based collective perception,

Reference 29

Resolution: verified exact; raw_fallback, observed 2026-08-06T18:57:32.270917Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1c5c56ac-2eae-4712-8059-6e5fb6e45e18 · outbound

This paper cites PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3d object detection,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising PV-RCNN++: Point-voxel feature set abstraction with local vector representation for 3d object detection,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.327307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 37e3e1e8-a2cf-43e8-9201-f63af9c4ed53 · outbound

This paper cites Cnn-based lidar point cloud de-noising in adverse weather,.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising Cnn-based lidar point cloud de-noising in adverse weather,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:57:32.309705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1ebb0e38-5623-49d7-97d8-e7584dece332 · outbound

This paper cites TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather.

DenoiseCP-Net: Efficient Collective Perception in Adverse Weather via Joint LiDAR-Based 3D Object Detection and Denoising TripleMixer: A 3D Point Cloud Denoising Model for Adverse Weather

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T18:57:32.112182Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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