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Paper Citation Record · LEDGER

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes

As of 19 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2412.00592.

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

pith.paper-citation-record.v1
2412.00592 v3

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:17:01.588598Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

32 of 32 outbound references displayed

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  • verified fuzzy26
  • unresolved6
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0030c80e-513a-412a-8cca-ffdaa29c0933 · outbound

This paper cites Denoising diffusion probabilistic models,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Denoising diffusion probabilistic models,

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ae9bca57-7cf4-4d4f-bcae-a6976f3ad53a · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 2

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no resolver link, observed 2026-08-12T05:17:01.468700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ada88419-c3d9-4934-829b-623ddb5dab9e · outbound

This paper cites Deep generative modeling of lidar data,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Deep generative modeling of lidar data,

Reference 3

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Source-reported events for the cited work

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Observation 0d9c7dbd-93d7-4cc2-949d-aa52a26b0b8a · outbound

This paper cites Learning to generate realistic lidar point clouds,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Learning to generate realistic lidar point clouds,

Reference 4

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ed93e531-14e1-4eeb-ad75-a527edcc6ebe · outbound

This paper cites Neural lidar fields for novel view synthesis,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Neural lidar fields for novel view synthesis,

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation bb592bd2-e83e-4cd3-8337-b60d33d7a55d · outbound

This paper cites A lidar point cloud generator: from a virtual world to autonomous driving,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes A lidar point cloud generator: from a virtual world to autonomous driving,

Reference 6

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Source-reported events for the cited work

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Observation 6db8c7f1-9d99-42fe-8a38-830848ad5c78 · outbound

This paper cites Unreal engine.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Unreal engine

Reference 7

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 095e2a4b-b556-4391-9235-a90e9e2c208e · outbound

This paper cites Unity: A General Platform for Intelligent Agents.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Unity: A General Platform for Intelligent Agents

Reference 8

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ec52814a-879b-4b3f-92e0-2567e65e8092 · outbound

This paper cites Carla: An open urban driving simulator,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Carla: An open urban driving simulator,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation dc56a634-cbc7-45f8-be8a-e7146880546c · outbound

This paper cites Lidarsim: Realistic lidar simulation by leveraging the real world,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Lidarsim: Realistic lidar simulation by leveraging the real world,

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 29606397-00c1-4850-b873-c3437dc71cf6 · outbound

This paper cites Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Towards zero domain gap: A comprehensive study of realistic lidar simulation for autonomy testing,

Reference 11

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 347a80e8-8e74-415b-ba02-88c277f0c86d · outbound

This paper cites LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Reference 12

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 0faba72a-476a-41b8-86f9-e931f9b6e688 · outbound

This paper cites Unisim: A neural closed-loop sensor simulator,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Unisim: A neural closed-loop sensor simulator,

Reference 13

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation cd4eb193-6e5d-4997-a605-d8eb5e96aa6c · outbound

This paper cites Nerf-lidar: Generating realistic lidar point clouds with neural radiance fields,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Nerf-lidar: Generating realistic lidar point clouds with neural radiance fields,

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f7850da3-6dfe-423a-a1ff-1ac3c52aecce · outbound

This paper cites Lidar4d: Dynamic neural fields for novel space-time view lidar synthesis,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Lidar4d: Dynamic neural fields for novel space-time view lidar synthesis,

Reference 15

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 6657edf2-3c1d-4cde-8b99-f60656128346 · outbound

This paper cites Dynamic lidar re-simulation using compositional neural fields,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Dynamic lidar re-simulation using compositional neural fields,

Reference 16

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 0b0f56db-d320-421e-84b7-6158429ac8aa · outbound

This paper cites Alignmif: Geometry-aligned multimodal implicit field for lidar-camera joint synthesis,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Alignmif: Geometry-aligned multimodal implicit field for lidar-camera joint synthesis,

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e398ebd9-4d7f-42bc-88b1-2d4b5bd04dff · outbound

This paper cites Towards realistic scene gener- ation with lidar diffusion models,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Towards realistic scene gener- ation with lidar diffusion models,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 2524c9c8-61ee-46e4-a4ea-ae0349ff8aae · outbound

This paper cites Learning compact representations for lidar completion and generation,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Learning compact representations for lidar completion and generation,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f4edc332-5962-4cbc-9809-166838570321 · outbound

This paper cites Maskgit: Masked generative image transformer,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Maskgit: Masked generative image transformer,

Reference 20

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ac692ded-e916-4fb8-88ae-d071ed0ad218 · outbound

This paper cites Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 6999a70a-0b14-4704-9d8c-bc0906430517 · outbound

This paper cites Lidardm: Generative lidar simulation in a generated world,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Lidardm: Generative lidar simulation in a generated world,

Reference 22

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no resolver link, observed 2026-08-12T05:17:01.549879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b9a36978-9b66-42de-8c1c-cd9650d6a32c · outbound

This paper cites Tulip: Transformer for upsampling of lidar point clouds,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Tulip: Transformer for upsampling of lidar point clouds,

Reference 23

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9b7757b5-e9e8-41c1-aded-ebd346e04b37 · outbound

This paper cites Scaling diffusion models to real-world 3d lidar scene completion,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Scaling diffusion models to real-world 3d lidar scene completion,

Reference 24

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation c5e88cf7-af24-435b-a072-17686f1fbe35 · outbound

This paper cites GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes GenMM: Geometrically and Temporally Consistent Multimodal Data Generation for Video and LiDAR

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1ceb3b95-12d0-4165-90dc-7e09bfb30dad · outbound

This paper cites Neural discrete representation learning,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Neural discrete representation learning,

Reference 26

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b1b7fc08-883c-4e47-858d-1312f719b551 · outbound

This paper cites Anchorformer: Point cloud completion from discriminative nodes,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Anchorformer: Point cloud completion from discriminative nodes,

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e4ff9dcd-3088-4719-8ef7-b1076e19f599 · outbound

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

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes nuscenes: A multimodal dataset for autonomous driving,

Reference 28

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9295f468-5eec-46be-89c5-35c46db6dc00 · outbound

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

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Scalability in perception for autonomous driving: Waymo open dataset,

Reference 29

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raw_fallback, observed 2026-08-12T05:17:01.825852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 72e70fa0-ba35-4630-abfb-a65a43261ddd · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes 4d spatio-temporal convnets: Minkowski convolutional neural networks,

Reference 30

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raw_fallback, observed 2026-08-12T05:17:01.813976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation a40c41c6-c484-4842-9194-5454991777b6 · outbound

This paper cites Search- ing efficient 3d architectures with sparse point-voxel convolution,.

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes Search- ing efficient 3d architectures with sparse point-voxel convolution,

Reference 31

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 35c9a403-1f35-4419-b3e7-579544ae7629 · outbound

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

LiDAR-EDIT: LiDAR Data Generation by Editing the Object Layouts in Real-World Scenes V oxelnext: Fully sparse voxelnet for 3d object detection and tracking,

Reference 32

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raw_fallback, observed 2026-08-12T05:17:01.790040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.