| Autor: |
Benjamin Kelenyi, Levente Tamas |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Access, Vol 11, Pp 7947-7958 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2023.3238901 |
| Popis: |
Point-cloud processing for extracting geometric features is difficult due to the highly non-linear rotation variance and measurement noise corrupting the data. To address these challenges, we propose a new architecture, called Dense 3D Geometric Features Extraction And Pose Estimation Using Self-Attention (D3GATTEN), which allows us to extract strong 3D features. Later on these can be used for point-cloud registration, object reconstruction, pose estimation, and tracking. The key contribution of our work is a new architecture that makes use of the self-attention module to extract powerful features. Thoughtful tests were performed on the 3DMatch dataset for point-cloud registration and on TUM RGB-D dataset for pose estimation achieving 98% Feature Matching Recall (FMR). Our results outperformed the existing state-of-the-art in terms of robustness specification for point-cloud alignment and pose estimation. Our code and test data can be accessed at link: https://github.com/tamaslevente/trai/tree/master/d3gatten. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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