Sammanfattning
We propose a novel multi-pose loss function to train a neural network for 6D pose estimation, using synthetic data and evaluating it on real images. Our loss is inspired by the VSD (Visible Surface Discrepancy) metric and relies on a differentiable renderer and CAD models. This novel multi-pose approach produces multiple weighted pose estimates to avoid getting stuck in local minima. Our method resolves pose ambiguities without using predefined symmetries. It is trained only on synthetic data. We test on real-world RGB images from the T-LESS dataset, containing highly symmetric objects common in industrial settings. We show that our solution can be used to replace the codebook in a state-of-the-art approach. So far, the codebook approach has had the shortest inference time in the field. Our approach reduces inference time further while a) avoiding discretization, b) requiring a much smaller memory footprint and c) improving pose recall.
Originalspråk | engelska |
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Titel på värdpublikation | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) |
Förlag | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Utgåva | 2021 |
ISBN (elektroniskt) | 978-1-6654-1714-3 |
ISBN (tryckt) | 978-1-6654-1715-0 |
DOI | |
Status | Published - 2021 sep. |
Evenemang | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 - Prague, Tjeckien Varaktighet: 2021 sep. 27 → 2021 okt. 1 |
Konferens
Konferens | IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021 |
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Land/Territorium | Tjeckien |
Ort | Prague |
Period | 2021/09/27 → 2021/10/01 |
Ämnesklassifikation (UKÄ)
- Datavetenskap (datalogi)