Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications | Paper Citation Record

Observation 541f4ea0-00f7-4d43-9090-19aaffe60d57 · outbound

This paper cites Field with the fragrant damask rose,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Field with the fragrant damask rose,

Reference 1

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Observation 2eba026e-9430-42de-a245-4b80d2107f9e · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 2

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Observation 21b6512e-2e42-41aa-9567-cbfa67ee93d3 · outbound

This paper cites Voxelnet: End-to-end learning for point cloud based 3d object detection,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Voxelnet: End-to-end learning for point cloud based 3d object detection,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.133979Z

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Observation 1f4c4971-2fff-4bed-999e-14d008ca2a26 · outbound

This paper cites Center- net: Keypoint triplets for object detection,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Center- net: Keypoint triplets for object detection,

Reference 4

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Observation 46c77bb3-1a8c-4e48-a723-ed5b006887d4 · outbound

This paper cites Objects as Points.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Objects as Points

Reference 5

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

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Frustum pointnets for 3d object detection from rgb-d data,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.116162Z

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This paper cites Voting for voting in online point cloud ob- ject detection.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Voting for voting in online point cloud ob- ject detection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.107306Z

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Observation a14d8d65-9c53-41af-979b-f96650724ecc · outbound

This paper cites 3d object proposals using stereo imagery for accurate object class detec- tion,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications 3d object proposals using stereo imagery for accurate object class detec- tion,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.099022Z

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Observation 3300d0fd-eda6-45f1-be08-b10357ba7763 · outbound

This paper cites Efficient joint segmen- tation, occlusion labeling, stereo and flow estimation,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Efficient joint segmen- tation, occlusion labeling, stereo and flow estimation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.090550Z

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Observation 4d92aa90-1a70-4d00-93bb-b41505c2a2fd · outbound

This paper cites Stereo r-cnn based 3d object detection for autonomous driving,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Stereo r-cnn based 3d object detection for autonomous driving,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.082208Z

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Observation 32558e48-59d9-4e3f-92c1-01f57b400c79 · outbound

This paper cites Faster r-cnn: Towards real- time object detection with region proposal networks,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Faster r-cnn: Towards real- time object detection with region proposal networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.073818Z

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Observation 647586d2-61c3-4d9c-b52f-5de587a0eb54 · outbound

This paper cites Monocular 3d object detection for autonomous driving,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Monocular 3d object detection for autonomous driving,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.063642Z

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Observation a692b3b0-1241-469a-8888-ff9b1d547d2e · outbound

This paper cites Monogrnet: A geometric reasoning net- work for monocular 3d object localization,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Monogrnet: A geometric reasoning net- work for monocular 3d object localization,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.055181Z

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Observation 7e4a7c70-91f3-4fbd-b78b-aec98a5dad58 · outbound

This paper cites Unsupervised cnn for single view depth estimation: Geometry to the rescue,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Unsupervised cnn for single view depth estimation: Geometry to the rescue,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.047584Z

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Observation 564a8c0b-f01c-4f5d-976d-242dd51f67a6 · outbound

This paper cites Unsupervised monoc- ular depth estimation with left-right consistency,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Unsupervised monoc- ular depth estimation with left-right consistency,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.038775Z

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Observation f01af081-0b05-48b3-a68f-f04c19f49b79 · outbound

This paper cites Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Using channel pruning-based yolo v4 deep learning algorithm for the real-time and accurate detection of apple flowers in natural environments,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.030719Z

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Observation 07c221e1-afdd-4d2d-bd14-ef0abc27987e · outbound

This paper cites Real-time apple detection system using embedded systems with hardware accelerators: An edge ai application,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Real-time apple detection system using embedded systems with hardware accelerators: An edge ai application,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.021873Z

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Observation 12b0df2a-7730-4ea4-847a-205dfe9b9229 · outbound

This paper cites Deep learning-based apple detection using a suppression mask r-cnn,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Deep learning-based apple detection using a suppression mask r-cnn,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.012866Z

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Observation e3c071e0-f0e0-4d5f-bef9-063e2418df16 · outbound

This paper cites Mask r-cnn,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Mask r-cnn,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:20.004731Z

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Observation 36f9029f-01d3-4f57-8269-e4709dca5d42 · outbound

This paper cites Flower classifica- tion using deep convolutional neural networks,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Flower classifica- tion using deep convolutional neural networks,

Reference 20

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Observation 3e5ab536-bcff-458b-9737-7fe8bbf7aead · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 21

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Observation b2047ae3-c20a-4ab3-8556-e934020ca79c · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Imagenet: A large-scale hierarchical image database,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-06T13:10:19.535003Z

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Observation 582f0d3a-77c9-42d7-9790-3d3321ed909c · outbound

This paper cites A visual vocabulary for flower classi- fication,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications A visual vocabulary for flower classi- fication,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.982604Z

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Observation fa45facd-ad37-4994-b87c-48380aa3ff85 · outbound

This paper cites Evaluation of model-based interactive flower recognition,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Evaluation of model-based interactive flower recognition,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.973639Z

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Observation a25c6791-4093-4963-9bb1-9a4071dbda46 · outbound

This paper cites Fruit detection, segmentation and 3d visuali- sation of environments in apple orchards,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Fruit detection, segmentation and 3d visuali- sation of environments in apple orchards,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.965148Z

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Observation 84a8ba1c-f995-4466-8e4a-e7a09b880d40 · outbound

This paper cites Flower detection using advanced deep learning techniques,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Flower detection using advanced deep learning techniques,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.956996Z

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Observation d2d06883-b892-4f2f-9e60-a46ba35e601c · outbound

This paper cites Apple, peach, and pear flower de- tection using semantic segmentation network and shape constraint level set,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Apple, peach, and pear flower de- tection using semantic segmentation network and shape constraint level set,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.948908Z

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This paper cites Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,

Reference 28

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This paper cites Coco-stuff: Thing and stuff classes in context,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Coco-stuff: Thing and stuff classes in context,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.934758Z

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Observation 829efb6f-a9a5-4b21-a586-e59b6b793fc8 · outbound

This paper cites Real-time detection of kiwifruit flower and bud simultane- ously in orchard using yolov4 for robotic pollination,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Real-time detection of kiwifruit flower and bud simultane- ously in orchard using yolov4 for robotic pollination,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.926330Z

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Observation 6cb350e4-5111-49e0-91c2-96ceaadae07d · outbound

This paper cites Image based mango fruit detection, localisation and yield estimation using multiple view geome- try,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Image based mango fruit detection, localisation and yield estimation using multiple view geome- try,

Reference 31

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Observation dc37d67e-4f20-49b2-a92b-b40aec1d8335 · outbound

This paper cites An automated fruit harvesting robot by using deep learn- ing,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications An automated fruit harvesting robot by using deep learn- ing,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.908896Z

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Observation 64452f92-7f5e-456b-a917-20c6899d3f39 · outbound

This paper cites Ssd: Single shot multibox detector,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Ssd: Single shot multibox detector,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.900876Z

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Observation b0b3f3b3-fcb0-45b7-ac1b-4b7113565d62 · outbound

This paper cites Robotic harvesting of rosa damascena using stereoscopic ma- chine vision,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Robotic harvesting of rosa damascena using stereoscopic ma- chine vision,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.892914Z

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Observation 2047260f-0a9f-4836-aafe-480a534f3201 · outbound

This paper cites Geometry-aware fruit grasping estimation for robotic har- vesting in apple orchards,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Geometry-aware fruit grasping estimation for robotic har- vesting in apple orchards,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.883892Z

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Observation c9e519df-a548-40b5-bcf6-003e62b3bcc5 · outbound

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

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Pointnet: Deep learning on point sets for 3d classification and segmentation,

Reference 36

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Observation fb432f10-36db-4ff5-8bbc-3d8e86f69a3a · outbound

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Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Octomap: An efficient probabilistic 3d mapping framework based on octrees,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.870948Z

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Observation 28af7ca7-6294-404b-a985-bffb533ab55f · outbound

This paper cites Rapid strawberry ripeness detection and 3d localization of picking point based on improved yolo v8-pose with rgb-camera,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Rapid strawberry ripeness detection and 3d localization of picking point based on improved yolo v8-pose with rgb-camera,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.862565Z

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Observation dd278f3d-6270-4176-9a75-9b866cd08165 · outbound

This paper cites Ultralytics YOLO,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Ultralytics YOLO,

Reference 39

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Observation 376faac2-ecf4-4ae2-a50d-b3eaff63be24 · outbound

This paper cites Efficientdet: Scalable and efficient object detection,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Efficientdet: Scalable and efficient object detection,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.849502Z

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Observation e23693a0-b095-40a5-96cb-67e4ec258d19 · outbound

This paper cites MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications MobileViTv3: Mobile-Friendly Vision Transformer with Simple and Effective Fusion of Local, Global and Input Features

Reference 41

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This paper cites Tomato pose estimation us- ing the association of tomato body and sepal,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Tomato pose estimation us- ing the association of tomato body and sepal,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.842011Z

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This paper cites Organization.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Organization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.833936Z

Source-reported events for the cited work

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Observation eec2e460-6631-4c55-a704-e14c7bb510e3 · outbound

This paper cites U-net: Convolutional net- works for biomedical image segmentation,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications U-net: Convolutional net- works for biomedical image segmentation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.826234Z

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This paper cites an unresolved cited work.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Unresolved cited work

Reference 45

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Observation c665ffb1-b629-41b7-a290-3833510ba6be · outbound

This paper cites Efficientnetv2: Smaller models and faster training,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Efficientnetv2: Smaller models and faster training,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.812518Z

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Observation 06fe87b8-599c-4cf1-9f62-3c94e999723b · outbound

This paper cites Going deeper with convolu- tions,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Going deeper with convolu- tions,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.804755Z

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Observation 14f133c3-1244-4a81-943f-07144a667fbe · outbound

This paper cites Cross-entropy loss functions: The- oretical analysis and applications,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Cross-entropy loss functions: The- oretical analysis and applications,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.796888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 180f339d-4c6c-4727-9fac-0f8bae5785c4 · outbound

This paper cites Fast r-cnn,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Fast r-cnn,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.788713Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f726ec59-1cee-4057-ba29-b9983fd5a98d · outbound

This paper cites Template matching,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Template matching,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.780844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7ad0932e-5c8e-48b9-b8dc-cedcf88264a6 · outbound

This paper cites Image rectification,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Image rectification,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.773536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 20aa1a98-7f61-4f53-9c27-aeb63da895fa · outbound

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

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Are we ready for autonomous driving? the kitti vision benchmark suite,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.765114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a971b6c4-d83c-4fc9-a2d7-972c164537ad · outbound

This paper cites Location of apples in trees using stereo- scopic vision,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Location of apples in trees using stereo- scopic vision,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.755686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f0bad0a6-0d95-4caf-af25-8b57fcda5fd5 · outbound

This paper cites De- formable convolutional networks,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications De- formable convolutional networks,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.745998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation be60961e-4179-4bc7-9475-7fc4ce9b2b59 · outbound

This paper cites Siamese neural net- works for one-shot image recognition,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Siamese neural net- works for one-shot image recognition,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.737434Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 595f6019-56c2-4648-8c75-e481b71ccbfe · outbound

This paper cites Widening siamese ar- chitectures for stereo matching,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Widening siamese ar- chitectures for stereo matching,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.728677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e43cb868-354e-4afe-b047-14583f414202 · outbound

This paper cites Dynamic feature fusion for visual object detection and segmen- tation,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Dynamic feature fusion for visual object detection and segmen- tation,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.718674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df5ec68e-4ceb-467f-b494-16d1d929fbd4 · outbound

This paper cites Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Self-Supervised Joint Learning Framework of Depth Estimation via Implicit Cues

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T13:10:19.635998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c748ea09-5364-4489-aa58-da898f069227 · outbound

This paper cites Transdssl: Trans- former based depth estimation via self-supervised learning,.

Sparse 3D Perception for Rose Harvesting Robots: A Two-Stage Approach Bridging Simulation and Real-World Applications Transdssl: Trans- former based depth estimation via self-supervised learning,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:10:19.709218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Reference resolution

Outbound references

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