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Paper Citation Record · LEDGER

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception

As of 8 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2606.02924.

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

pith.paper-citation-record.v1
2606.02924 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-28T14:47:30.097347Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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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 fuzzy0
  • unresolved32
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

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Outbound references

Observation d9a6fd0f-811f-46a6-92a9-744a60869649 · outbound

This paper cites Convnet-generated adversarial perturbations for evaluating 3d object detection robustness.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Convnet-generated adversarial perturbations for evaluating 3d object detection robustness

Reference 1

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:7270d658b96ffb4305dded66cc10b1a6c35affc5ee9a94c97b0c91b428dbfba0

Observation ce89a0d0-97a7-4024-a3ee-1884e636aa22 · outbound

This paper cites Zenseact open dataset: A large-scale and diverse multimodal dataset for autonomous driving.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Zenseact open dataset: A large-scale and diverse multimodal dataset for autonomous driving

Reference 2

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:ee965d076d06b441a354503dc3341c23a1abaefc12dd0d26ee48e8ddeb1d14e1

Observation 826f86a3-b457-4919-a672-9b90b8ec0a50 · outbound

This paper cites Argoverse 2: Next generation datasets for self-driving perception and forecasting.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Argoverse 2: Next generation datasets for self-driving perception and forecasting

Reference 3

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Observation 02195eaa-13eb-48a9-8c68-2b3c99ffd607 · outbound

This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

Reference 4

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:e6790122b98fedb3d023bc29f8c2fee22f93d04736382e66d5a87dd7311a551e

Observation 4f359699-b4d4-4740-b75a-bdb72dd3adea · outbound

This paper cites You can’t see me: Physical removal attacks on lidar-based autonomous vehicles driving frameworks.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception You can’t see me: Physical removal attacks on lidar-based autonomous vehicles driving frameworks

Reference 5

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:a99dc5693269f284681044f9386f425219cb527535e6cf8397ea4526e9e54b4d

Observation 2d6bf475-a175-4177-b048-211838eb2dd5 · outbound

This paper cites Morley Mao.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Morley Mao

Reference 6

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:dfd045f77ecec06ea882a129f2e87707c995d6be283b6b925610185b59747420

Observation 31c3fad2-6823-41c6-b4de-c6a2367e9338 · outbound

This paper cites MMDetection3D: OpenMMLab next-generation platform for general 3D object detection.https://github.com/open-mmlab/mmdetection3d, 2020.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception MMDetection3D: OpenMMLab next-generation platform for general 3D object detection.https://github.com/open-mmlab/mmdetection3d, 2020

Reference 7

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:d8395d1ba17d94420c2879a90c947fe57d6ada0b74c8ab7ef101135f7f5a3c74

Observation 3ed1de87-a04f-47c7-b056-e61a59b9b316 · outbound

This paper cites Benchmarking robustness of 3d object detection to common corruptions.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Benchmarking robustness of 3d object detection to common corruptions

Reference 8

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Observation bb53dce5-21c1-4e01-9711-ff06fe66df59 · outbound

This paper cites Vision meets robotics: The kitti dataset.The international journal of robotics research, 32(11):1231–1237, 2013.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Vision meets robotics: The kitti dataset.The international journal of robotics research, 32(11):1231–1237, 2013

Reference 9

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:4b70c1da9a04befdbe52a768a46de8243006a2725ba3ddbfe86641a233f1fc02

Observation c990cf8f-1664-4344-9ce4-c35d9b33cbd2 · outbound

This paper cites Msf: Motion-guided sequential fusion for efficient 3d object detection from point cloud sequences.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Msf: Motion-guided sequential fusion for efficient 3d object detection from point cloud sequences

Reference 10

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:d7ec810f4eeba6b55c8fa5bb94e1971bd8b2a80e24a60a375e7a306da408f321

Observation 48b7f3ec-b881-4164-8f15-01b785ab222d · outbound

This paper cites Ptt: Point-trajectory transformer for efficient temporal 3d object detection.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Ptt: Point-trajectory transformer for efficient temporal 3d object detection

Reference 11

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Observation b3c532f8-bf30-4ab0-a4c7-1c88b2e0e103 · outbound

This paper cites Pla-lidar: Physical laser attacks against lidar-based 3d object detection in autonomous vehicle.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Pla-lidar: Physical laser attacks against lidar-based 3d object detection in autonomous vehicle

Reference 12

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:638a7f7df460cd6ddf6e6dd1d049b254cd98b023ca60ee4e604a5d42458d1d7e

Observation ba654b8f-8232-4dab-a0ee-e2ef9735cd08 · outbound

This paper cites Phantomlidar: Cross- modality signal injection attacks against lidar.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Phantomlidar: Cross- modality signal injection attacks against lidar

Reference 13

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:e5ef6d5071f8d45aa0a4e278bf9eda390b84bb4111347b7f9fd915b33222649d

Observation be9a0110-99e8-47b4-a853-0bcc2f7b1970 · outbound

This paper cites Invisible but detected: Physical adversarial shadow attack and defense on LiDAR object detection.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Invisible but detected: Physical adversarial shadow attack and defense on LiDAR object detection

Reference 14

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:6e105aac1b07f37fbd09ce695839efcf5fc2eaf2ccf5327cec7b2272a0c8c479

Observation 689f6629-524a-40ac-bb1b-47c1ec78d588 · outbound

This paper cites Lang, Sourabh V ora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Lang, Sourabh V ora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom

Reference 15

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:299c3083c5bf56443ca2251935a2ccaccefcda972084909acb55cd93fb44ea68

Observation 0285123f-24e8-4095-a4fc-46717d0357f8 · outbound

This paper cites Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion

Reference 16

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Observation 573db8fe-a8a9-4e21-a468-614fbb3a3dab · outbound

This paper cites Li et al.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Li et al

Reference 17

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:8c7146fe5d3ee7f696c25012b09da864ab96bf05f7f3491abe6136150f2f3284

Observation b694781f-e5a1-46e7-9e3d-5f65deda1938 · outbound

This paper cites Practical removal attacks on lidar-based object detection in autonomous driving.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Practical removal attacks on lidar-based object detection in autonomous driving

Reference 18

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Observation e14240cc-dad2-4c4b-b38e-b97bf93408cf · outbound

This paper cites On the realism of lidar spoofing attacks against autonomous driving vehicle at high speed and long distance.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception On the realism of lidar spoofing attacks against autonomous driving vehicle at high speed and long distance

Reference 19

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:5dcf878de3c8363f2e809cd384e48f80121b5cfd84b179bc1f7c4e1270ac99a4

Observation 44717e4c-869c-434d-8b84-0ce4d6575a67 · outbound

This paper cites Morley Mao.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Morley Mao

Reference 20

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:1209fc0222f9f439297139087a0ccf97b5b5f67fa210f4ff861d10ff20d08859

Observation 3d41685b-37c5-4908-90a4-78412d3b373e · outbound

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

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Scalability in perception for autonomous driving: Waymo open dataset

Reference 21

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Observation 87667dc0-a2b0-498c-8a5f-d13c5eb946cd · outbound

This paper cites Wip: Towards practical lidar spoofing attack against vehicles driving at cruising speeds.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Wip: Towards practical lidar spoofing attack against vehicles driving at cruising speeds

Reference 22

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Observation 86d9182d-d0ac-42bf-a097-b4f6b97b937d · outbound

This paper cites Openpcdet: An open-source toolbox for 3d object detection from point clouds.https://github.com/open-mmlab/OpenPCDet, 2020.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Openpcdet: An open-source toolbox for 3d object detection from point clouds.https://github.com/open-mmlab/OpenPCDet, 2020

Reference 23

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Observation 88d6fb63-15e6-4b1c-9441-3e79afb51d81 · outbound

This paper cites Physically realizable adversarial examples for lidar object detection, 2020.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Physically realizable adversarial examples for lidar object detection, 2020

Reference 24

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Observation a5e172f8-8e86-41b1-9007-32eae0d97146 · outbound

This paper cites Dsvt: Dynamic sparse voxel transformer with rotated sets.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Dsvt: Dynamic sparse voxel transformer with rotated sets

Reference 25

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Observation ba3de6ae-e129-481d-a436-0bd86ee12239 · outbound

This paper cites V2x-real: A large-scale dataset for vehicle-to-everything cooperative perception.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception V2x-real: A large-scale dataset for vehicle-to-everything cooperative perception

Reference 26

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Observation b2062254-01bd-43a3-a8b1-7e0ca85e84c4 · outbound

This paper cites Second: Sparsely embedded convolutional detection.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Second: Sparsely embedded convolutional detection

Reference 27

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Observation 4e35202c-1eab-4b72-be77-6a58b69a923c · outbound

This paper cites Center-based 3d object detection and tracking.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Center-based 3d object detection and tracking

Reference 28

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:dfb78461187fe7a509ac29df363952567fc4a358d22398dac8df22b22f4404f4

Observation d193a2a5-b79f-4c86-b422-5bded53a521d · outbound

This paper cites Zhang, Glen Chou, and Saibal Mukhopadhyay.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Zhang, Glen Chou, and Saibal Mukhopadhyay

Reference 29

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Observation 453b178f-3f65-49bc-be6c-5c6c6ff072b9 · outbound

This paper cites Centerformer: Center-based transformer for 3d object detection.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Centerformer: Center-based transformer for 3d object detection

Reference 30

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:98266233d9c324900b795a526c9499ed65e60f65bdd23e4b26bb9fd168c7253f

Observation d7f57f18-f6ed-42da-ab4d-ac9ce2488339 · outbound

This paper cites Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

Reference 31

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arxiv_id, observed 2026-07-01T23:06:19.526054Z

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source=pdf_text observed=2026-06-28T14:47:30.097347Z digest=sha256:9ea6534a331a0addd70c19f2e1d3e89bb2fb1e3e73ae907a25d073e1261d0915

Observation 4a4800e3-fbfb-4cd1-895f-7ce0ba935f69 · outbound

This paper cites Roscenes: A large-scale multi-view 3d dataset for roadside perception.

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception Roscenes: A large-scale multi-view 3d dataset for roadside perception

Reference 32

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Observation 12f6e3d2-a0c1-454c-8dfb-1897e58558fa · outbound

This paper cites To ensure that spoofed observations propagate through the detection pipeline realistically, we recompute region proposals using CP4F [28].

ATLAS: A Large-Scale Evaluation Benchmark for Adversarial LiDAR Perception To ensure that spoofed observations propagate through the detection pipeline realistically, we recompute region proposals using CP4F [28]

Reference 33

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Pith citing papers

No inbound Pith citation observations are available.