SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation | Paper Citation Record

Observation 94e14341-0191-4ac3-b66b-258cf22136b6 · outbound

This paper cites ”Stereo vision-Facing the challenges and seeing the op- portunities for ADAS applications.” Texas Instruments Technical Note (2016).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Stereo vision-Facing the challenges and seeing the op- portunities for ADAS applications.” Texas Instruments Technical Note (2016)

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.844994Z

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Observation f859ef0c-bcd1-479d-b6d3-73f2ef360fb5 · outbound

This paper cites ”Distance measurement system for au- tonomous vehicles using stereo camera.” Array 5 (2020): 100016.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Distance measurement system for au- tonomous vehicles using stereo camera.” Array 5 (2020): 100016

Reference 2

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Observation 2d98ea0b-3299-4fc8-9b69-10edcad1c896 · outbound

This paper cites ”Spatial pyramid pooling in deep convolutional networks for visual recognition.” IEEE transactions on pattern analysis and machine intelligence 37.9 (2015): 1904-1916.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Spatial pyramid pooling in deep convolutional networks for visual recognition.” IEEE transactions on pattern analysis and machine intelligence 37.9 (2015): 1904-1916

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.848937Z

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Observation 0230977c-658e-4214-9552-22821d1a6273 · outbound

This paper cites Vision-based over-height vehicle detection for warning drivers.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation Vision-based over-height vehicle detection for warning drivers

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.852284Z

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Observation 9f4a03fc-9213-422f-9cac-3ca4d64ff44f · outbound

This paper cites ” 车载限高障碍物检测系统的设计与实现.” 电光系统 2 (2018): 13-17.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ” 车载限高障碍物检测系统的设计与实现.” 电光系统 2 (2018): 13-17

Reference 6

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Observation fd4f529f-4f67-47da-8757-f3abc554fb79 · outbound

This paper cites ” 激光雷达辅助驾驶道路参数计算方法研究.” 应用光学 41.1 (2020): 209.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ” 激光雷达辅助驾驶道路参数计算方法研究.” 应用光学 41.1 (2020): 209

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.889366Z

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Observation a33b2c47-ca18-4d94-b760-ac67bec90a8a · outbound

This paper cites 车载道路限制几何信息测量和超高预警方法研究.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation 车载道路限制几何信息测量和超高预警方法研究

Reference 8

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Observation 82437666-3c4a-4923-a53f-e63ea2a1c3d8 · outbound

This paper cites ”Detection of individual trees and estimation of tree height using LiDAR data.” Journal of Forest Research 12.6 (2007): 425-434.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Detection of individual trees and estimation of tree height using LiDAR data.” Journal of Forest Research 12.6 (2007): 425-434

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.661884Z

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Observation 42707242-321e-4d14-8465-872bf2802169 · outbound

This paper cites an unresolved cited work.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-05-24T10:34:21.815286Z

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Observation f94537bc-6b35-4ef7-be5e-c75b4123c40e · outbound

This paper cites ”Crop height monitoring with digital imagery from Unmanned Aerial System (UAS).” Computers and Electronics in Agriculture 141 (2017): 232-237.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Crop height monitoring with digital imagery from Unmanned Aerial System (UAS).” Computers and Electronics in Agriculture 141 (2017): 232-237

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.827021Z

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Observation 83c7b89b-a7fb-42e7-819d-cf11eee803f8 · outbound

This paper cites ”Biomass and crop height estimation of different crops using UA V-based LiDAR.” Remote Sensing 12.1 (2019): 17.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Biomass and crop height estimation of different crops using UA V-based LiDAR.” Remote Sensing 12.1 (2019): 17

Reference 12

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Observation b16e0360-a75d-4955-ae17-8babf7fd5f85 · outbound

This paper cites ”Wheat height estimation using LiDAR in compar- ison to ultrasonic sensor and UAS.” Sensors 18.11 (2018): 3731.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Wheat height estimation using LiDAR in compar- ison to ultrasonic sensor and UAS.” Sensors 18.11 (2018): 3731

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.836232Z

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Observation 38935cb6-fddb-433e-abca-bc85584cc458 · outbound

This paper cites ”Regression kriging for improving crop height models fusing ultra-sonic sensing with UA V imagery.” Remote Sensing 9.7 (2017): 665.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Regression kriging for improving crop height models fusing ultra-sonic sensing with UA V imagery.” Remote Sensing 9.7 (2017): 665

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.778415Z

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Observation 30d05916-7f85-4417-b118-68f1a99457d7 · outbound

This paper cites ”Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles.” Remote Sensing of Environment 268 (2022): 112760.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles.” Remote Sensing of Environment 268 (2022): 112760

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.791680Z

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Observation 1ce8ffb2-7fa5-4422-897b-8015b7b0d0cd · outbound

This paper cites ”Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information process- ing systems 28 (2015).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Faster r-cnn: Towards real-time object detection with region proposal networks.” Advances in neural information process- ing systems 28 (2015)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.799186Z

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Observation b136216f-0e95-42f1-956c-f5dcb820664e · outbound

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

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-24T10:34:20.450861Z

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Observation f43c2260-8e27-4d7e-b163-3599ae343569 · outbound

This paper cites ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.782059Z

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Observation e3948a2e-07e3-4292-a5d6-813949879ce0 · outbound

This paper cites ”Mobilenetv2: Inverted residuals and linear bot- tlenecks.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Mobilenetv2: Inverted residuals and linear bot- tlenecks.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.766053Z

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Observation 9fa72d02-4579-446c-a791-32b3d5296ff7 · outbound

This paper cites ”Searching for mobilenetv3.” Proceedings of the IEEE/CVF international conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Searching for mobilenetv3.” Proceedings of the IEEE/CVF international conference on computer vision

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.770197Z

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Observation 4a4bd9c8-e86d-4d3a-a910-d9d812aa850c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-05-24T10:34:20.464150Z

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Observation 99ce0935-4afa-477b-83db-bddc8b3f899e · outbound

This paper cites ”Swin transformer: Hierarchical vision transformer using shifted windows.” Proceedings of the IEEE/CVF International Conference on Computer Vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Swin transformer: Hierarchical vision transformer using shifted windows.” Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.774513Z

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Observation 78023170-da44-4ee6-a21a-c1f240f72cae · outbound

This paper cites ”Vivit: A video vision transformer.” Proceedings of the IEEE/CVF International Conference on Computer Vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Vivit: A video vision transformer.” Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.760831Z

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Observation 938fe082-faf1-4d9a-9c5d-8c7dd60be2a6 · outbound

This paper cites ”Fully convolu- tional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Fully convolu- tional networks for semantic segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.822942Z

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Observation 94e39b7c-166d-4077-a44c-ed1474bb990b · outbound

This paper cites ”U-net: Convo- lutional networks for biomedical image segmentation.” International Con- ference on Medical image computing and computer-assisted intervention.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”U-net: Convo- lutional networks for biomedical image segmentation.” International Con- ference on Medical image computing and computer-assisted intervention

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.819155Z

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Observation 7ac4729d-9ddf-4317-8234-d62c463511dc · outbound

This paper cites ”Unet++: A nested u-net architecture for medical image segmentation.” Deep learning in medical image analysis and multimodal learning for clinical decision support.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Unet++: A nested u-net architecture for medical image segmentation.” Deep learning in medical image analysis and multimodal learning for clinical decision support

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.811101Z

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Observation c0c4a39f-b3af-42e4-9fb3-1c7c870b2dca · outbound

This paper cites ”H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes.” IEEE transactions on medical imaging 37.12 (2018): 2663-2674.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes.” IEEE transactions on medical imaging 37.12 (2018): 2663-2674

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.840691Z

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Observation 86df4429-a85f-4776-97e4-e826adc116fd · outbound

This paper cites ”Robust object tracking with online multiple instance learning.” IEEE transactions on pattern analysis and machine intelligence 33.8 (2010): 1619-1632.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Robust object tracking with online multiple instance learning.” IEEE transactions on pattern analysis and machine intelligence 33.8 (2010): 1619-1632

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.787776Z

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Observation 96ff405d-2730-4a06-bb3b-e5410dec4adc · outbound

This paper cites ”High-speed tracking with kernelized cor- relation ﬁlters.” IEEE transactions on pattern analysis and machine intelligence 37.3 (2014): 583-596.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”High-speed tracking with kernelized cor- relation ﬁlters.” IEEE transactions on pattern analysis and machine intelligence 37.3 (2014): 583-596

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.795390Z

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Observation ba37f3b1-2c1e-4189-88d0-82119ae83252 · outbound

This paper cites ”Discriminative correlation ﬁlter with channel and spatial reliability.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Discriminative correlation ﬁlter with channel and spatial reliability.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.803182Z

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Observation 313b7b68-247d-4778-83a8-be00280144ed · outbound

This paper cites ”Visual object tracking using adaptive correlation ﬁlters.” 2010 IEEE computer society conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Visual object tracking using adaptive correlation ﬁlters.” 2010 IEEE computer society conference on computer vision and pattern recognition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.752441Z

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Observation 3686dd59-8575-4d9f-8bc2-44ebf797fd8c · outbound

This paper cites ”Real-time tracking via on-line boosting.” Bmvc.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Real-time tracking via on-line boosting.” Bmvc

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.748212Z

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Observation db731081-4478-44aa-bac0-42ec41e662f3 · outbound

This paper cites ”Forward- backward error: Automatic detection of tracking failures.” 2010 20th international conference on pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Forward- backward error: Automatic detection of tracking failures.” 2010 20th international conference on pattern recognition

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.756746Z

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Observation fdf6da80-7b83-4841-b014-f5c0ec4697d3 · outbound

This paper cites ”Depth map prediction from a single image using a multi-scale deep network.” Advances in neural information processing systems 27 (2014).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Depth map prediction from a single image using a multi-scale deep network.” Advances in neural information processing systems 27 (2014)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.709293Z

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Observation 5d2efaeb-e32f-48c6-9e65-8bdb1b4dd264 · outbound

This paper cites ”Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture.” Proceedings of the IEEE international conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture.” Proceedings of the IEEE international conference on computer vision

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.715084Z

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Observation f568c972-13f4-4290-9d5b-e66408747bb0 · outbound

This paper cites ”Deep ordinal regression network for monocular depth estimation.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Deep ordinal regression network for monocular depth estimation.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.732384Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53762d1a-291b-46fa-a66a-b78f4ffa71a6 · outbound

This paper cites ”Adabins: Depth estimation using adaptive bins.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Adabins: Depth estimation using adaptive bins.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.695900Z

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Observation 4f7a5edf-0018-4663-90aa-96d051dde129 · outbound

This paper cites an unresolved cited work.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-05-24T10:34:21.699868Z

Source-reported events for the cited work

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Observation c1f6fc77-7986-4fdd-8cd4-323ab37c346f · outbound

This paper cites ”Unsupervised monocular depth estimation using attention and multi-warp reconstruc- tion.” IEEE Transactions on Multimedia (2021).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Unsupervised monocular depth estimation using attention and multi-warp reconstruc- tion.” IEEE Transactions on Multimedia (2021)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.686821Z

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Observation 49639922-2621-4362-a23e-cc65b8ae9350 · outbound

This paper cites ”Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras.” Proceedings of the IEEE/CVF International Conference on Computer Vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras.” Proceedings of the IEEE/CVF International Conference on Computer Vision

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.691309Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ee44b857-7bed-43fc-9d1d-a072514de912 · outbound

This paper cites ”Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera.” 2019 International Conference on Robotics and Automation (ICRA).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera.” 2019 International Conference on Robotics and Automation (ICRA)

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.704368Z

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Observation ad6dfd6c-a84d-4b4e-a442-f7c8254b4101 · outbound

This paper cites ”Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving.” IEEE Transactions on Intelligent Transportation Systems (2021).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Self-Supervised Depth Completion From Direct Visual-LiDAR Odometry in Autonomous Driving.” IEEE Transactions on Intelligent Transportation Systems (2021)

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.681951Z

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Observation 5739deed-d44f-4b81-bcdc-87289d964587 · outbound

This paper cites ”Selfdeco: Self-supervised monocular depth com- pletion in challenging indoor environments.” 2021 IEEE International Conference on Robotics and Automation (ICRA).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Selfdeco: Self-supervised monocular depth com- pletion in challenging indoor environments.” 2021 IEEE International Conference on Robotics and Automation (ICRA)

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.885335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 35d7c05e-9204-4635-953d-52b16a006d70 · outbound

This paper cites ”Learning rich features from RGB-D images for object detection and segmentation.” European conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Learning rich features from RGB-D images for object detection and segmentation.” European conference on computer vision

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.737148Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ca072ff6-43bc-4981-a663-4aec29808619 · outbound

This paper cites ”Multimodal deep learning for robust RGB-D ob- ject recognition.” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Multimodal deep learning for robust RGB-D ob- ject recognition.” 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.744075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b7cec8b-d524-4b66-8a2e-496af12818ad · outbound

This paper cites ”CANet: Co-attention network for RGB-D semantic segmentation.” Pattern Recognition 124 (2022): 108468.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”CANet: Co-attention network for RGB-D semantic segmentation.” Pattern Recognition 124 (2022): 108468

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.634658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3d6e015b-9a4c-4d29-8b5b-3b9d9b59a82b · outbound

This paper cites ”Intrinsic scene properties from a single rgb-d image.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Intrinsic scene properties from a single rgb-d image.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.620454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 306ccd30-1270-4ace-ba07-c352c0f7b985 · outbound

This paper cites ”Single image depth estimation from predicted semantic labels.” 2010 IEEE computer society conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Single image depth estimation from predicted semantic labels.” 2010 IEEE computer society conference on computer vision and pattern recognition

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.627387Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 06616317-a478-4eb8-b8e1-7da2d79e145b · outbound

This paper cites ”Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance.” European Conference on Computer Vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance.” European Conference on Computer Vision

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.623791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0fc32e8d-1a9b-4bf4-828f-328a38a047f9 · outbound

This paper cites ”Robust object proposals re- ranking for object detection in autonomous driving using convolutional neural networks.” Signal Processing: Image Communication 53 (2017): 110-122.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Robust object proposals re- ranking for object detection in autonomous driving using convolutional neural networks.” Signal Processing: Image Communication 53 (2017): 110-122

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.630947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e3c7ce7-209c-4be7-bcdc-fef096d516a9 · outbound

This paper cites ”Data-driven 3d voxel patterns for object category recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Data-driven 3d voxel patterns for object category recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.638212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3d8588e2-1128-42c1-8d5c-cb4867be6de7 · outbound

This paper cites ”Pointnet: Deep learning on point sets for 3d classiﬁcation and segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Pointnet: Deep learning on point sets for 3d classiﬁcation and segmentation.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.617374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 303a2617-ce10-45b5-b604-54a0b42e9aaa · outbound

This paper cites ”Pointnet++: Deep hierarchical feature learning on point sets in a metric space.” Advances in neural information processing systems 30 (2017).

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Pointnet++: Deep hierarchical feature learning on point sets in a metric space.” Advances in neural information processing systems 30 (2017)

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.643488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a3bd65d8-31f9-47c5-8858-fea03e1bc398 · outbound

This paper cites ”Frustum pointnets for 3d object detection from rgb-d data.” Proceedings of the IEEE conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Frustum pointnets for 3d object detection from rgb-d data.” Proceedings of the IEEE conference on computer vision and pattern recognition

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.879304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4c8d1b26-f5ab-4137-8e12-2164fda506cb · outbound

This paper cites ”Pointnetlk: Robust & efﬁcient point cloud registration using pointnet.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Pointnetlk: Robust & efﬁcient point cloud registration using pointnet.” Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.807079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c83b3309-ecc9-4572-abe3-3313b60a68ee · outbound

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

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation CoBEVT: Cooperative Bird's Eye View Semantic Segmentation with Sparse Transformers

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-24T10:34:20.458676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 05e03af1-21f2-4acc-a649-8e6bd2e267d4 · outbound

This paper cites V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-24T10:34:20.473191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7a953898-de7c-4f55-8a53-6c909c303e70 · outbound

This paper cites an unresolved cited work.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-05-24T10:34:21.720804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 3f2450f3-d7ff-4957-a7d7-78a193dc2e14 · outbound

This paper cites ”Centernet: Keypoint triplets for object detection.” Proceedings of the IEEE/CVF international conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Centernet: Keypoint triplets for object detection.” Proceedings of the IEEE/CVF international conference on computer vision

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.893239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6df08f51-c311-475d-9ec4-100eac76c013 · outbound

This paper cites ”Focal loss for dense object detection.” Proceedings of the IEEE international conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Focal loss for dense object detection.” Proceedings of the IEEE international conference on computer vision

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.654014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9bbc6d5d-a360-457e-9d13-4b60e1644de0 · outbound

This paper cites ”Fcos: Fully convolutional one-stage object detection.” Proceedings of the IEEE/CVF international conference on computer vision.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”Fcos: Fully convolutional one-stage object detection.” Proceedings of the IEEE/CVF international conference on computer vision

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.666979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8b907f94-d703-4fa9-85ce-24a8da10c723 · outbound

This paper cites ”On estimation of a probability density function and mode.” The annals of mathematical statistics 33.3 (1962): 1065-1076.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”On estimation of a probability density function and mode.” The annals of mathematical statistics 33.3 (1962): 1065-1076

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.671929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 831dd91b-75a5-42c3-9353-ba73613f4bf9 · outbound

This paper cites ”A new approach to linear ﬁltering and predic- tion problems.” (1960): 35-45.

SHLE: Devices Tracking and Depth Filtering for Stereo-based Height Limit Estimation ”A new approach to linear ﬁltering and predic- tion problems.” (1960): 35-45

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T10:34:21.649563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers