A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects | Paper Citation Record

Observation 4311f603-43d9-4682-9fbe-646e6f684786 · outbound

This paper cites Dae-gan: Dynamic aspect-aware gan for text-to-image synthesis,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Dae-gan: Dynamic aspect-aware gan for text-to-image synthesis,

Reference 1

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This paper cites Cpws: Confident programmatic weak supervision for high-quality data labeling,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Cpws: Confident programmatic weak supervision for high-quality data labeling,

Reference 2

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This paper cites Color enhanced cross correlation net for image sentiment analysis,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Color enhanced cross correlation net for image sentiment analysis,

Reference 3

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This paper cites Embodied intelli- gence toward future smart manufacturing in the era of ai foundation model,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Embodied intelli- gence toward future smart manufacturing in the era of ai foundation model,

Reference 4

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This paper cites Embodied intel- ligence via learning and evolution,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Embodied intel- ligence via learning and evolution,

Reference 5

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This paper cites Multi-modal 3d object detection in autonomous driving: a survey,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-modal 3d object detection in autonomous driving: a survey,

Reference 6

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This paper cites Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-modal 3d object detection in autonomous driving: A survey and taxonomy,

Reference 7

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Observation 687399b9-64cb-4553-bdb9-50d1fc7b4a41 · outbound

This paper cites Multi-sensor fusion and cooperative perception for autonomous driv- ing: A review,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-sensor fusion and cooperative perception for autonomous driv- ing: A review,

Reference 8

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This paper cites Camera, lidar, and imu based multi-sensor fusion slam: A survey,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Camera, lidar, and imu based multi-sensor fusion slam: A survey,

Reference 9

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Observation 0e554796-3a4f-4ab6-845f-da5a091bca3d · outbound

This paper cites Multi-modality 3d object detection in autonomous driving: A review,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-modality 3d object detection in autonomous driving: A review,

Reference 10

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Observation 90a330c2-da2e-48c2-a431-fbfc6b82cf99 · outbound

This paper cites Advancements in perception system with multi-sensor fusion for embodied agents,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Advancements in perception system with multi-sensor fusion for embodied agents,

Reference 11

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Observation ef42c4cc-525c-4f34-9a2d-7e3afba3c346 · outbound

This paper cites A survey on the visual perception of humanoid robot,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects A survey on the visual perception of humanoid robot,

Reference 12

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Observation 29d92456-73dc-4d7d-8032-658d7c9f4ec1 · outbound

This paper cites Robustness-aware 3d object detection in autonomous driving: A review and outlook,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Robustness-aware 3d object detection in autonomous driving: A review and outlook,

Reference 13

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This paper cites Multimodal fusion and vision-language models: A survey for robot vision,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multimodal fusion and vision-language models: A survey for robot vision,

Reference 14

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Observation a3858fab-7614-41e9-8ac5-26db34f794bb · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 15

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Observation eb9dea81-f650-446c-af29-329a12ea7831 · outbound

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

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects nuscenes: A multimodal dataset for autonomous driving,

Reference 16

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Observation b72f43a5-04ed-4994-aeec-e9b8fe0f02f8 · outbound

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

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Scalability in perception for autonomous driving: Waymo open dataset,

Reference 17

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Observation cda5e536-1b81-4ee6-b69a-77445d7fbdf9 · outbound

This paper cites Cityscapes 3d: Dataset and benchmark for 9 dof vehicle detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Cityscapes 3d: Dataset and benchmark for 9 dof vehicle detection,

Reference 18

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This paper cites Argoverse: 3d tracking and forecasting with rich maps,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Argoverse: 3d tracking and forecasting with rich maps,

Reference 19

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Observation 17922f8d-3364-4ce7-87a4-2445bfe9e9ec · outbound

This paper cites A*3d: An autonomous driving dataset in challenging environments,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects A*3d: An autonomous driving dataset in challenging environments,

Reference 20

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Observation fe1d813b-4c81-49c7-8b3f-f3dd50ba9efd · outbound

This paper cites The apolloscape dataset for autonomous driving,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects The apolloscape dataset for autonomous driving,

Reference 21

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Observation fa35d388-b095-4f0e-8e6c-7f7e125df1c8 · outbound

This paper cites Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Not All Datasets Are Born Equal: On Heterogeneous Data and Adversarial Examples

Reference 22

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Observation fd02ac52-497e-4808-9f77-8b53ba8e926c · outbound

This paper cites The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects The h3d dataset for full-surround 3d multi-object detection and tracking in crowded urban scenes,

Reference 23

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Observation 7a1fa5b0-58b9-43df-9caa-b0b9e1b09940 · outbound

This paper cites The Cityscapes Dataset for Semantic Urban Scene Understanding.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects The Cityscapes Dataset for Semantic Urban Scene Understanding

Reference 24

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Observation 8504f790-c2d4-432f-99eb-73174a680525 · outbound

This paper cites Pointfusion: Deep sensor fusion for 3d bounding box estimation,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pointfusion: Deep sensor fusion for 3d bounding box estimation,

Reference 25

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Observation 89261c17-f4be-4b4e-93f0-1b7ee0706ab7 · outbound

This paper cites Pointpainting: Sequential fusion for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pointpainting: Sequential fusion for 3d object detection,

Reference 26

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Observation 432fcb4e-451c-4a1a-ae68-f993f4398600 · outbound

This paper cites Multimodal virtual point 3d de- tection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multimodal virtual point 3d de- tection,

Reference 27

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Observation f25c02c5-9a1c-4fd9-9149-781d5f8de676 · outbound

This paper cites Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Deepfusion: Lidar-camera deep fusion for multi-modal 3d object detection,

Reference 28

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Observation eff4c427-b478-488f-97b7-caaed8d23aca · outbound

This paper cites Centerfusion: Center-based radar and camera fusion for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Centerfusion: Center-based radar and camera fusion for 3d object detection,

Reference 29

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Observation 75844b1c-be3b-4136-937f-a4d77a07ea82 · outbound

This paper cites Pointaugmenting: Cross- modal augmentation for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pointaugmenting: Cross- modal augmentation for 3d object detection,

Reference 30

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Observation 66e0dd46-1422-4d2f-a5cd-86abf20b14d2 · outbound

This paper cites Unifying voxel-based representation with transformer for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Unifying voxel-based representation with transformer for 3d object detection,

Reference 31

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This paper cites Sparse fuse dense: Towards high quality 3d detection with depth completion,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sparse fuse dense: Towards high quality 3d detection with depth completion,

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Observation ae2d9e14-3d47-456e-8f5b-1e253e636f31 · outbound

This paper cites Joint 3d proposal generation and object detection from view aggregation,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Joint 3d proposal generation and object detection from view aggregation,

Reference 33

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Observation 0474d767-0c9a-47d0-8305-2791cd16f7da · outbound

This paper cites Roarnet: A robust 3d object detection based on region approximation refinement,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Roarnet: A robust 3d object detection based on region approximation refinement,

Reference 34

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This paper cites Weakly aligned cross-modal learning for multispectral pedestrian detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Weakly aligned cross-modal learning for multispectral pedestrian detection,

Reference 35

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Observation 3328c9fa-e8a6-4b28-84d2-7345dbe5d4bc · outbound

This paper cites E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects E2E-MFD: Towards End-to-End Synchronous Multimodal Fusion Detection

Reference 36

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Observation 9eb9fba1-05a4-4495-8851-0aa3c777434e · outbound

This paper cites Mvx-net: Multimodal voxelnet for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Mvx-net: Multimodal voxelnet for 3d object detection,

Reference 37

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Observation 404c8279-b13c-4911-969c-2a553a22fb9f · outbound

This paper cites Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Bridging the view disparity between radar and camera features for multi-modal fusion 3d object detection,

Reference 38

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Observation c95d35d3-9505-40f6-a104-cb3a1a9106c3 · outbound

This paper cites Improving multispectral pedestrian detection by addressing modality imbalance problems,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Improving multispectral pedestrian detection by addressing modality imbalance problems,

Reference 39

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Observation 19a8dec4-7c7f-472e-ba35-c7cb56035fa0 · outbound

This paper cites Multimodal object detection by channel switching and spatial attention,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multimodal object detection by channel switching and spatial attention,

Reference 40

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Observation 4653c6c9-3c37-475a-99f1-73d27b21281a · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 41

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Observation f53df29e-aa38-47a8-91d6-ff63ea296082 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pointnet++: Deep hierarchical feature learning on point sets in a metric space,

Reference 42

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Observation 9c2df701-0fa6-489a-b435-5b979c730c56 · outbound

This paper cites Frustum pointnets for 3d object detection from rgb-d data,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Frustum pointnets for 3d object detection from rgb-d data,

Reference 43

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Observation 1b90b121-b862-4fd3-81db-cab5ea5832ac · outbound

This paper cites Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Pi-rcnn: An efficient multi-sensor 3d object detector with point-based attentive cont-conv fusion module,

Reference 44

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Observation 570c83bb-9f00-4a06-abba-730ccdb6de87 · outbound

This paper cites Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Fusionpainting: Multimodal fusion with adaptive attention for 3d object detection,

Reference 45

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Observation 2a8d6a96-4f63-4a14-9880-cb3f5e63e257 · outbound

This paper cites Graphalign: Enhanc- ing accurate feature alignment by graph matching for multi-modal 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Graphalign: Enhanc- ing accurate feature alignment by graph matching for multi-modal 3d object detection,

Reference 46

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Observation 750f3a87-ebe8-494c-bf3b-6aced675150f · outbound

This paper cites Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Vpfnet: Improving 3d object detection with virtual point based lidar and stereo data fusion,

Reference 47

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Observation 3a020289-19f8-4382-82d5-66b621cb12eb · outbound

This paper cites V oxel field fusion for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects V oxel field fusion for 3d object detection,

Reference 48

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Observation 0e3f95e7-0d11-422a-8b60-b86a3728178c · outbound

This paper cites AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects AutoAlign: Pixel-Instance Feature Aggregation for Multi-Modal 3D Object Detection

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.313624Z

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Observation 642d6a30-1da5-49f6-9a16-5400084aea07 · outbound

This paper cites Deformable feature aggregation for dynamic multi-modal 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Deformable feature aggregation for dynamic multi-modal 3d object detection,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.317898Z

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Observation 9b5db611-bbed-474b-b666-03eec225d1b9 · outbound

This paper cites VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection

Reference 51

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Observation 4c54e19a-89e4-43f2-aec7-dfdce680d44f · outbound

This paper cites Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Transfusion: Robust lidar-camera fusion for 3d object detection with transformers,

Reference 52

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Observation 2127434c-02b5-418c-aedb-d9fa3ee07492 · outbound

This paper cites Learning cross-modal deep representations for robust pedestrian detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Learning cross-modal deep representations for robust pedestrian detection,

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Observation 6a4ebc54-41b5-4539-8814-f8ba6b3a6af1 · outbound

This paper cites Guided attentive feature fusion for multispectral pedestrian detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Guided attentive feature fusion for multispectral pedestrian detection,

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.332815Z

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Observation f02aef6e-8b19-4174-95dc-278b9b35e152 · outbound

This paper cites Removal and selection: Improving rgb-infrared object detection via coarse-to-fine fusion,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Removal and selection: Improving rgb-infrared object detection via coarse-to-fine fusion,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.336592Z

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Observation 353af0f4-c004-47a2-b546-67e65a888e0b · outbound

This paper cites Deep continuous fusion for multi-sensor 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Deep continuous fusion for multi-sensor 3d object detection,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.340404Z

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Observation 74bad30a-b55c-40dc-8cc6-2549ae2fea77 · outbound

This paper cites Cross-modality 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Cross-modality 3d object detection,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.344214Z

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Observation eb457e84-4dc4-4a8b-a97b-4e28de714f15 · outbound

This paper cites Multi-task multi-sensor fusion for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-task multi-sensor fusion for 3d object detection,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.348213Z

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Observation 711a68c9-fa63-45c9-aaea-4503f7524d0e · outbound

This paper cites Epnet: Enhancing point features with image semantics for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Epnet: Enhancing point features with image semantics for 3d object detection,

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.352056Z

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Observation d03677b7-ddab-4fbb-823b-d155da50546f · outbound

This paper cites Epnet++: Cas- cade bi-directional fusion for multi-modal 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Epnet++: Cas- cade bi-directional fusion for multi-modal 3d object detection,

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.356455Z

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Unavailable: canonical work link unavailable.

Observation 7fde655f-e55a-400c-967b-669f95f96004 · outbound

This paper cites Dense voxel fusion for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Dense voxel fusion for 3d object detection,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.360926Z

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Observation 337dfc6d-ee99-4061-96d2-6455b4c033f6 · outbound

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

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Logonet: Towards accurate 3d object detection with local-to-global cross-modal fusion,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.365350Z

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Observation 84ec30f2-e99d-4b64-be70-df337d0a3646 · outbound

This paper cites Cat-det: Contrastively augmented transformer for multi-modal 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Cat-det: Contrastively augmented transformer for multi-modal 3d object detection,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.369218Z

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Observation 788f3f34-4505-490e-94b1-cc44abc4b494 · outbound

This paper cites Sea- date: Remedy dual-attention transformer with semantic alignment via contrast learning for multimodal object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sea- date: Remedy dual-attention transformer with semantic alignment via contrast learning for multimodal object detection,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.373639Z

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Observation 0abf2c25-84ff-402c-aa00-a06f9b753589 · outbound

This paper cites Fusion-Mamba for Cross-modality Object Detection.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Fusion-Mamba for Cross-modality Object Detection

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.377787Z

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Observation 2c33071e-d68b-47a1-8403-77db50fc3195 · outbound

This paper cites CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.383488Z

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Observation 94c0090d-c792-4fe4-9043-525a189312d6 · outbound

This paper cites Collaboration helps camera overtake lidar in 3d detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Collaboration helps camera overtake lidar in 3d detection,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.388002Z

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Unavailable: canonical work link unavailable.

Observation 3372e87a-1edb-42e4-96ae-4577aac4d92c · outbound

This paper cites V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects V2vnet: Vehicle-to-vehicle communication for joint perception and prediction,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.392089Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 223aaa45-9f97-4986-8037-fe7bb247f81b · outbound

This paper cites an unresolved cited work.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.396722Z

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Unavailable: canonical work link unavailable.

Observation 6adaddc3-e541-48c7-8457-963678b54442 · outbound

This paper cites Macp: Efficient model adaptation for cooperative perception,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Macp: Efficient model adaptation for cooperative perception,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.400510Z

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Observation c317ab31-19c4-4003-8db8-f828eba831f7 · outbound

This paper cites Hm-vit: Hetero-modal vehicle-to-vehicle cooperative perception with vision transformer,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Hm-vit: Hetero-modal vehicle-to-vehicle cooperative perception with vision transformer,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.404732Z

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Observation 0ad79f15-a6fe-4ace-bc3e-de45e6f8b322 · outbound

This paper cites Multi-agent collaborative perception via motion-aware robust communication network,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Multi-agent collaborative perception via motion-aware robust communication network,

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.408891Z

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Unavailable: canonical work link unavailable.

Observation d65055bf-bdcd-411d-b056-8cf8f64a6c73 · outbound

This paper cites When2com: Multi-agent perception via communication graph grouping,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects When2com: Multi-agent perception via communication graph grouping,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.412873Z

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Unavailable: canonical work link unavailable.

Observation a77039fb-b03b-44e1-a96f-780e935686fc · outbound

This paper cites Who2com: Collaborative perception via learnable handshake com- munication,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Who2com: Collaborative perception via learnable handshake com- munication,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.416852Z

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Unavailable: canonical work link unavailable.

Observation 446cd771-3b63-41ea-a6b5-a756576ab26e · outbound

This paper cites How2comm: Communication-efficient and collaboration- pragmatic multi-agent perception,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects How2comm: Communication-efficient and collaboration- pragmatic multi-agent perception,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.420710Z

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Observation 92e072d2-b584-4c79-946a-0dec1ae46eb1 · outbound

This paper cites Communication- efficient collaborative perception via information filling with code- book,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Communication- efficient collaborative perception via information filling with code- book,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.424447Z

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Observation 65fb6c8e-a7ab-446c-94ca-bd1ec8808126 · outbound

This paper cites Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.428348Z

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Unavailable: canonical work link unavailable.

Observation 6d4a1f77-6940-4a03-ae2d-0359424c510c · outbound

This paper cites Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective supervision,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.431942Z

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Observation 19396dbf-ade6-4bb5-a25e-fead444ee430 · outbound

This paper cites Exploring object- centric temporal modeling for efficient multi-view 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Exploring object- centric temporal modeling for efficient multi-view 3d object detection,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.437333Z

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Observation 22ceabec-9675-4295-8ca3-d39758b64847 · outbound

This paper cites Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.441392Z

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Observation dbd7e775-7366-457e-b201-68788dc587c4 · outbound

This paper cites Sparse4D v2: Recurrent Temporal Fusion with Sparse Model.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sparse4D v2: Recurrent Temporal Fusion with Sparse Model

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.445435Z

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Observation 66aa7e0c-8efc-4a91-805c-ebf83f3c532c · outbound

This paper cites Sparse4d v3: Advancing end-to-end 3d detection and tracking,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sparse4d v3: Advancing end-to-end 3d detection and tracking,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.450294Z

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Observation e8b5a7a3-345d-4958-8afc-2b692c8aa4b3 · outbound

This paper cites Sparsefusion3d: Sparse sensor fusion for 3d object detection by radar and camera in environmental perception,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Sparsefusion3d: Sparse sensor fusion for 3d object detection by radar and camera in environmental perception,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.454825Z

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Observation 7dea2c14-91fb-4d6f-8f9b-d2315407f495 · outbound

This paper cites Planning-oriented autonomous driving,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Planning-oriented autonomous driving,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.458780Z

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Observation e1435bc5-0bf8-4bd2-b6eb-3f9a932b3e6b · outbound

This paper cites FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects FusionAD: Multi-modality Fusion for Prediction and Planning Tasks of Autonomous Driving

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.462747Z

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Observation 1cd89c83-7ebb-452c-9cd0-7b533f713423 · outbound

This paper cites Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Rcbevdet: Radar-camera fusion in bird’s eye view for 3d object detection,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.467206Z

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Observation 6d772103-cc83-4224-9213-58a059225807 · outbound

This paper cites Vision-centric bev perception: A survey,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Vision-centric bev perception: A survey,

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:01.050283Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 049d072f-0111-4fd5-91c2-b3f621a09527 · outbound

This paper cites End-to-end object detection with transformers,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects End-to-end object detection with transformers,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.475723Z

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Observation e007338f-a0dc-405b-a85e-c39b55c27243 · outbound

This paper cites Deformable DETR: Deformable transformers for end-to-end object detection,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Deformable DETR: Deformable transformers for end-to-end object detection,

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:01.026014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 964efb1b-1cea-499f-bf85-ff32bf682b70 · outbound

This paper cites Detr3d: 3d object detection from multi-view images via 3d- to-2d queries,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Detr3d: 3d object detection from multi-view images via 3d- to-2d queries,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.484800Z

Source-reported events for the cited work

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Observation 21acf919-eb71-40f0-9973-b1286bfccf31 · outbound

This paper cites Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly estimating depth,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly estimating depth,

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:01.004836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 163f9942-8207-48e4-981e-a39e1cccfe54 · outbound

This paper cites BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.494120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce11b22e-a837-4247-8457-963fc35fc0de · outbound

This paper cites Bevdet: High- performance multi-camera 3d object detection in bird-eye-view,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Bevdet: High- performance multi-camera 3d object detection in bird-eye-view,

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.990805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d1abb3b-0da4-42c3-9aef-78293f98b257 · outbound

This paper cites BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.503489Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 515291d6-1086-489c-ae55-2b29a4fd9e85 · outbound

This paper cites Unifusion: Unified multi-view fusion transformer for spatial-temporal representation in bird’s-eye-view,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Unifusion: Unified multi-view fusion transformer for spatial-temporal representation in bird’s-eye-view,

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.973947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4a8cabde-9d50-4241-8095-a16fb26950aa · outbound

This paper cites Tbp-former: Learning temporal bird’s-eye-view pyramid for joint perception and prediction in vision-centric autonomous driving,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Tbp-former: Learning temporal bird’s-eye-view pyramid for joint perception and prediction in vision-centric autonomous driving,

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.959619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8422bf3-87bf-4995-a6c8-6cedcfd64df4 · outbound

This paper cites HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects HVDetFusion: A Simple and Robust Camera-Radar Fusion Framework

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-15T18:27:59.516415Z

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Unavailable: canonical work link unavailable.

Observation 1996df73-1c99-431e-82aa-5a9dc66122e2 · outbound

This paper cites Mutr3d: A multi- camera tracking framework via 3d-to-2d queries,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Mutr3d: A multi- camera tracking framework via 3d-to-2d queries,

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.946890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e1cd53c5-75fb-4bcf-8d22-a64108702112 · outbound

This paper cites Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi-object tracking,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi-object tracking,

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.934218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation e8325c01-f197-49c0-a904-2c213558e743 · outbound

This paper cites Fusionformer: A concise unified feature fusion transformer for 3D pose estimation,.

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects Fusionformer: A concise unified feature fusion transformer for 3D pose estimation,

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:28:00.921644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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