Projekter pr. år
Abstrakt
We present a method for generating synthetic ground truth for training segmentation networks for presegmenting point clouds in pose estimation problems. Our method replaces global pose estimation algorithms such as RANSAC which requires manual fine-tuning with a robust CNN, without having to hand-label segmentation masks for the given object. The data is generated by blending cropped images of the objects with arbitrary backgrounds. We test the method in two scenarios, and show that networks trained on the generated data segments the objects with high accuracy, allowing them to be used in a pose estimation pipeline.
Originalsprog | Engelsk |
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Titel | Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP |
Redaktører | Giovanni Maria Farinella, Petia Radeva, Jose Braz |
Vol/bind | 4 |
Forlag | SCITEPRESS Digital Library |
Publikationsdato | 2020 |
Sider | 482-489 |
ISBN (Elektronisk) | 978-989-758-402-2 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Valletta, Malta Varighed: 27. feb. 2020 → 29. feb. 2020 Konferencens nummer: 15 |
Konference
Konference | 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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Nummer | 15 |
Land/Område | Malta |
By | Valletta |
Periode | 27/02/2020 → 29/02/2020 |
Navn | VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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Vol/bind | 4 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Synthetic Ground Truth for Presegmentation of Known Objects for Effortless Pose Estimation'. Sammen danner de et unikt fingeraftryk.Relaterede Projekter
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