Paper Citation Record · LEDGER

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain

As of 12 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2501.04950.

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

pith.paper-citation-record.v1
2501.04950 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T21:25:54.123303Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

34 of 34 outbound references displayed

verified exact2
verified fuzzy24
unresolved7
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bc27c40a-869d-478e-a1f3-5700bfc009c5 · outbound

This paper cites FCOS: Fully convolutional one- stage object detection,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain FCOS: Fully convolutional one- stage object detection,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.634626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0757b92a-ace5-457f-b2de-92bc171ce980 · outbound

This paper cites SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.623826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 72262531-8aee-4a5c-8b96-e0ba11304c03 · outbound

This paper cites DETR3D: 3D Object Detection from Multi-view Images via 3D-to- 2D Queries,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain DETR3D: 3D Object Detection from Multi-view Images via 3D-to- 2D Queries,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.613540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f6327ffd-ace0-4f72-af94-59e8a23834e7 · outbound

This paper cites PointPillars: Fast Encoders for Object Detection from Point Clouds,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain PointPillars: Fast Encoders for Object Detection from Point Clouds,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.602934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5062709b-88f6-4757-a806-9e69b1b0bf70 · outbound

This paper cites SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain SSN: Shape Signature Networks for Multi-class Object Detection from Point Clouds,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.592869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b6db8052-a867-410d-890e-3abe779b38d1 · outbound

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

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Center-based 3d object detection and tracking,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.583435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 81295be6-b06d-48c8-8858-41c7d85e2deb · outbound

This paper cites RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain RadarDistill: Boosting Radar-based Object Detection Performance via Knowledge Distillation from LiDAR Features

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:25:54.355973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8bd14e90-2bf4-44f5-ab24-7e57b2284b95 · outbound

This paper cites CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain CRN: Camera Radar Net for Accurate, Robust, Efficient 3D Perception,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.573520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f906f7fd-90cb-42a2-bd0e-f5ee46c387e8 · outbound

This paper cites BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.563127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation db1d6c83-6895-4bb7-8e37-c70cd61341cd · outbound

This paper cites Cross Modal Transformer: Towards Fast and Robust 3D Object Detection.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Cross Modal Transformer: Towards Fast and Robust 3D Object Detection

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.040765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d378aa5-feca-422f-839a-60dc71a035cc · outbound

This paper cites ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.552778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 825bdf8d-99b5-4f32-b19f-a0b489533934 · outbound

This paper cites ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T21:25:54.326237Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7b02fad6-fbab-4b94-9609-2ec882d5a72c · outbound

This paper cites Playing for benchmarks,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Playing for benchmarks,

Reference 13

Resolution: malformed identifier; no resolver link, observed 2026-08-10T21:25:54.052083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94458310-3bb1-4097-b3ed-ca85175e0c13 · outbound

This paper cites Virtual KITTI 2,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Virtual KITTI 2,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.541299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5a259f4e-d365-4937-bdd7-454c10549943 · outbound

This paper cites Are we ready for autonomous driving? The KITTI vision benchmark suite,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Are we ready for autonomous driving? The KITTI vision benchmark suite,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.530197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f8af0461-879c-45e2-8f5d-6ab3e1f8b665 · outbound

This paper cites Temporal coherence for active learning in videos,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Temporal coherence for active learning in videos,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.519268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8350f360-8087-45de-9f45-4ae569fe4a44 · outbound

This paper cites SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.506100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 13e5c16a-7835-42e0-b45e-f980fdd9e663 · outbound

This paper cites OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communication,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.068728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34fd7862-4729-4117-aa61-8447286d28e5 · outbound

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

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain nuScenes: A multimodal dataset for autonomous driving,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.494650Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7db018a3-2a32-4cd6-856f-069467a0f1bd · outbound

This paper cites Playing for Data: Ground Truth from Computer Games,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Playing for Data: Ground Truth from Computer Games,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.482192Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 220ebfe9-9f47-4253-af04-c056a8b737b0 · outbound

This paper cites Virtual worlds as proxy for multi-object tracking analysis,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Virtual worlds as proxy for multi-object tracking analysis,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.077320Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84112062-6192-4291-92a8-6fe8a70d65c5 · outbound

This paper cites A theory of learning from different domains,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain A theory of learning from different domains,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.465130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bec8d38a-cee1-4719-b0c9-00994fa153bb · outbound

This paper cites UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain UADA3D: Unsupervised Adversarial Domain Adaptation for 3D Object Detection with Sparse LiDAR and Large Domain Gaps

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.083931Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9799f331-caf5-4820-9c8f-134d66b030bd · outbound

This paper cites Scalability in Perception for Autonomous Driving: Waymo Open Dataset,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Scalability in Perception for Autonomous Driving: Waymo Open Dataset,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.452989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c32b1336-420c-4c9e-ad42-e35c8983bc2a · outbound

This paper cites ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.442475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 6daed8ff-5bad-4cdc-b404-b341390aef3b · outbound

This paper cites On Learning Invariant Representations for Domain Adaptation,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain On Learning Invariant Representations for Domain Adaptation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.431358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0b665ae0-70a2-4c3e-a961-cea3ca07f230 · outbound

This paper cites Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self- Supervision,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self- Supervision,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.420089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation be446fdb-7497-46a7-9a29-80205474c452 · outbound

This paper cites FDA: Fourier Domain Adaptation for Semantic Segmentation.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain FDA: Fourier Domain Adaptation for Semantic Segmentation

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.101404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc2d3435-9765-4d9a-835c-646e877dac41 · outbound

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

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain CARLA: An open urban driving simulator,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.409090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3b3f2a4c-6564-4a37-9679-bea0b9f8d803 · outbound

This paper cites MORAI Simulator,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain MORAI Simulator,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.398574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3d5bc980-0426-405a-ab42-19aebe5e6017 · outbound

This paper cites Ministry of land,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Ministry of land,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.386664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d7afcc0e-aefc-42f6-8364-d600ba5a16ea · outbound

This paper cites Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T21:25:54.374533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b906383d-e035-44b4-a997-7711f41953bd · outbound

This paper cites MMDetection: Open MMLab Detection Toolbox and Benchmark.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain MMDetection: Open MMLab Detection Toolbox and Benchmark

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.119173Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91bcb8ce-5315-4f0e-9b88-70a39e202b4f · outbound

This paper cites MMDetection3D: OpenMMLab next-generation platform for general 3D object detection,.

MORDA: A Synthetic Dataset to Facilitate Adaptation of Object Detectors to Unseen Real-target Domain While Preserving Performance on Real-source Domain MMDetection3D: OpenMMLab next-generation platform for general 3D object detection,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T21:25:54.123303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.