| Nome |
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A computer vision approach based on deep learning for the detection of dairy cows in 2 free stall barn, file e1dcb337-215b-7715-e053-1705fe0a6cc9
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267
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Monocular Depth Perception on Microcontrollers for Edge Applications, file 6a1c409f-285a-445d-a95e-cae5a41dc854
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93
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Enabling Image-Based Streamflow Monitoring at the Edge, file e1dcb335-ef1a-7715-e053-1705fe0a6cc9
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75
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Beyond the Baseline: 3D Reconstruction of Tiny Objects with Single Camera Stereo Robot, file e1dcb338-66a3-7715-e053-1705fe0a6cc9
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68
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Quantitative Evaluation of Confidence Measures in a Machine Learning World, file e1dcb338-90dd-7715-e053-1705fe0a6cc9
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67
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Depth Restoration in Under-Display Time-of-Flight Imaging, file c8bd307b-064f-4b25-907c-eaac06a8ff70
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54
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Real-time self-adaptive deep stereo, file e1dcb338-7548-7715-e053-1705fe0a6cc9
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48
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Learning monocular depth estimation infusing traditional stereo knowledge, file e1dcb338-4624-7715-e053-1705fe0a6cc9
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46
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Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs, file d971c453-b2c5-4ee8-9343-b8ab5d6f37a2
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45
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Confidence Estimation for ToF and Stereo Sensors and Its Application to Depth Data Fusion, file d3e68419-fe3c-44a1-a55e-6a4d7704b968
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44
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On the deployment of out-of-the-box embedded devices for self-powered river surface flow velocity monitoring at the edge, file e1dcb337-ff38-7715-e053-1705fe0a6cc9
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24
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Open Challenges in Deep Stereo: the Booster Dataset, file e2596895-60e8-4cb0-a1cc-6c2447c7e8ef
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24
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Leveraging a weakly adversarial paradigm for joint learning of disparity and confidence estimation, file 4f27879c-83d2-436a-bba6-f07175f62ef5
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22
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Continual Adaptation for Deep Stereo, file 93915365-f362-4347-a1d6-d9dd91d86d5e
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21
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Monitoring Social Distancing With Single Image Depth Estimation, file e1dcb33a-247b-7715-e053-1705fe0a6cc9
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20
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Real-Time Single Image Depth Perception in the Wild with Handheld Devices, file e1dcb336-abdc-7715-e053-1705fe0a6cc9
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18
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RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation, file 1c237929-4620-4424-bc0d-d6c235185339
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16
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On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey, file 88924056-6a64-4dff-9761-428b23b372fa
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16
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On the confidence of stereo matching in a deep-learning era: a quantitative evaluation, file cb120ca9-a888-4a08-bc21-baf79c1ed5b2
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16
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Good cues to learn from scratch a confidence measure for passive depth sensors, file 1db5a2db-596f-4101-be0d-76d0886aca81
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12
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Unsupervised Adaptation for Deep Stereo, file e1dcb330-5fc9-7715-e053-1705fe0a6cc9
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12
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Enabling monocular depth perception at the very edge, file 17eedc31-55da-4c7b-8416-89b3938c0313
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11
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Unsupervised Domain Adaptation for Depth Prediction from Images, file 69d33512-e4e2-433f-b1be-0b5709aaaf57
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11
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Real-Time Semantic Stereo Matching, file f63a2fbd-3ee5-4575-9d34-692eadbe192d
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11
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Distilled semantics for comprehensive scene understanding from videos, file 2d16da9b-8332-4a40-95bb-c01a307ad91c
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9
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Booster: A Benchmark for Depth From Images of Specular and Transparent Surfaces, file 49b54555-e5b7-4d2c-a611-1ab99253d95c
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9
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Sensor-guided optical flow, file 2e5ee9db-8a85-43d7-9794-f0301dc15a3c
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8
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Distilled semantics for comprehensive scene understanding from videos, file ce4f47b4-f4d2-41e6-abfc-eb79c936c971
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8
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Learning confidence measures in the wild, file e1dcb330-5fca-7715-e053-1705fe0a6cc9
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8
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ScanNeRF: a Scalable Benchmark for Neural Radiance Fields, file 95784217-93dd-49e3-b641-97bdf76adc67
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7
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On the Uncertainty of Self-Supervised Monocular Depth Estimation, file e1dcb335-c9f3-7715-e053-1705fe0a6cc9
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7
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Learning optical flow from still images, file 2c9ba3ed-675e-4e36-8c27-39f23e53cb08
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6
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On the Uncertainty of Self-Supervised Monocular Depth Estimation, file c89bc652-bc35-49ac-9122-ab6c6df46cda
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6
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Towards real-time unsupervised monocular depth estimation on CPU, file e1dcb331-de48-7715-e053-1705fe0a6cc9
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5
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Enabling monocular depth perception at the very edge, file 6f2d8b26-5506-48dd-bf78-d32370210c79
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4
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Learning to Predict Stereo Reliability Enforcing Local Consistency of Confidence Maps, file e1dcb330-30c9-7715-e053-1705fe0a6cc9
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4
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Beyond local reasoning for stereo confidence estimation with deep learning, file e1dcb331-c39e-7715-e053-1705fe0a6cc9
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4
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Generative Adversarial Networks for unsupervised monocular depth prediction, file e1dcb331-c3a0-7715-e053-1705fe0a6cc9
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4
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Good cues to learn from scratch a confidence measure for passive depth sensors, file e1dcb336-0a8e-7715-e053-1705fe0a6cc9
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4
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Monocular Depth Perception on Microcontrollers for Edge Applications, file 1387fa48-1495-4e9b-9767-9a915eaf9f2a
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3
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Sparsity Agnostic Depth Completion, file 899117fc-1898-4a8b-94ee-703621885e27
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3
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Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation, file c9415970-dd46-42c6-87e4-b6ac8ee5a837
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3
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Quantitative Evaluation of Confidence Measures in a Machine Learning World, file e1dcb330-5fc7-7715-e053-1705fe0a6cc9
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3
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Geometry meets semantic for semi-supervised monocular depth estimation, file e1dcb331-9690-7715-e053-1705fe0a6cc9
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3
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Enhancing Self-Supervised Monocular Depth Estimation with Traditional Visual Odometry, file e1dcb333-53b0-7715-e053-1705fe0a6cc9
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3
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Matching-space Stereo Networks for Cross-domain Generalization, file e1dcb336-16ae-7715-e053-1705fe0a6cc9
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3
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The Monocular Depth Estimation Challenge, file 1645efda-4b45-4e14-8022-8cdb391f914b
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2
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Open Challenges in Deep Stereo: the Booster Dataset, file 5848de1c-3bbd-4466-85f6-06c4e055fe20
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2
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Learning from scratch a confidence measure, file e1dcb32f-8175-7715-e053-1705fe0a6cc9
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2
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Leveraging confident points for accurate depth refinement on embedded systems, file e1dcb333-910c-7715-e053-1705fe0a6cc9
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2
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Guided stereo matching, file e1dcb333-9115-7715-e053-1705fe0a6cc9
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2
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On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey, file 0e2e4ea2-1394-4047-b427-6bad868b05d9
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1
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Multi-View Guided Multi-View Stereo, file 3dc06d14-1ed4-4f58-b130-04e78d0287f7
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1
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Distilled semantics for comprehensive scene understanding from videos, file 561e0d07-3015-4ba7-808a-8a628c038633
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1
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Sensor-guided optical flow, file 5aca6db0-2b6e-46ff-bb4f-fee875978d27
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1
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Learning Depth Estimation for Transparent and Mirror Surfaces, file 61121869-68da-4abe-9776-53921e54a707
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1
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RGB-Multispectral Matching: Dataset, Learning Methodology, Evaluation, file 89531579-4af5-49f2-bcd4-65742274a4a0
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1
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Self-adapting confidence estimation for stereo, file a9977d82-d5b3-43b2-b360-b537bc462a7e
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1
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Multi-View Guided Multi-View Stereo, file b0d94f43-652a-4e7d-af1b-40aa0474a7b6
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1
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Leveraging a weakly adversarial paradigm for joint learning of disparity and confidence estimation, file b7c8618d-576d-4979-92a3-0a52c66f922d
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1
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Real-Time Semantic Stereo Matching, file b80320a7-e6c4-4db1-ac64-68ae56b39b4c
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1
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Unsupervised confidence for LiDAR depth maps and applications, file c6796703-72ba-4c93-8c20-8a858f6bf945
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1
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Depth super-resolution from explicit and implicit high-frequency features, file d7997543-6bf7-4b07-853c-918399fe63f0
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1
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Even More Confident Predictions with Deep Machine-Learning, file e1dcb330-7f18-7715-e053-1705fe0a6cc9
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1
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Learning Monocular Depth Estimation with Unsupervised Trinocular Assumptions, file e1dcb331-de3f-7715-e053-1705fe0a6cc9
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1
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Confidence Estimation for ToF and Stereo Sensors and Its Application to Depth Data Fusion, file e1dcb333-7590-7715-e053-1705fe0a6cc9
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1
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Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms, file e1dcb333-a6be-7715-e053-1705fe0a6cc9
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1
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Learning end-to-end scene flow by distilling single tasks knowledge, file e1dcb336-cc21-7715-e053-1705fe0a6cc9
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1
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A computer vision approach based on deep learning for the detection of dairy cows in 2 free stall barn, file e1dcb337-e636-7715-e053-1705fe0a6cc9
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1
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Neural Disparity Refinement for Arbitrary Resolution Stereo, file e1dcb339-3ace-7715-e053-1705fe0a6cc9
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1
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Energy-Quality Scalable Monocular Depth Estimation on Low-Power CPUs, file e1dcb339-f3db-7715-e053-1705fe0a6cc9
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1
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NTIRE 2023 Challenge on HR Depth From Images of Specular and Transparent Surfaces, file fe5dac23-16eb-4493-945f-61d3068c150e
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1
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| Totale |
1.186 |