Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929237066252288 |
|---|---|
| author | Pöllabauer, Thomas Rücker, Fabian Franek, Andreas Gorschlüter, Felix |
| author_facet | Pöllabauer, Thomas Rücker, Fabian Franek, Andreas Gorschlüter, Felix |
| contents | Current state-of-the-art 6d pose estimation is too compute intensive to be deployed on edge devices, such as Microsoft HoloLens (2) or Apple iPad, both used for an increasing number of augmented reality applications. The quality of AR is greatly dependent on its capabilities to detect and overlay geometry within the scene. We propose a synthetically trained client-server-based augmented reality application, demonstrating state-of-the-art object pose estimation of metallic and texture-less industry objects on edge devices. Synthetic data enables training without real photographs, i.e. for yet-to-be-manufactured objects. Our qualitative evaluation on an AR-assisted sorting task, and quantitative evaluation on both renderings, as well as real-world data recorded on HoloLens 2, sheds light on its real-world applicability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_04979 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training Pöllabauer, Thomas Rücker, Fabian Franek, Andreas Gorschlüter, Felix Computer Vision and Pattern Recognition Artificial Intelligence Current state-of-the-art 6d pose estimation is too compute intensive to be deployed on edge devices, such as Microsoft HoloLens (2) or Apple iPad, both used for an increasing number of augmented reality applications. The quality of AR is greatly dependent on its capabilities to detect and overlay geometry within the scene. We propose a synthetically trained client-server-based augmented reality application, demonstrating state-of-the-art object pose estimation of metallic and texture-less industry objects on edge devices. Synthetic data enables training without real photographs, i.e. for yet-to-be-manufactured objects. Our qualitative evaluation on an AR-assisted sorting task, and quantitative evaluation on both renderings, as well as real-world data recorded on HoloLens 2, sheds light on its real-world applicability. |
| title | Detection and Pose Estimation of flat, Texture-less Industry Objects on HoloLens using synthetic Training |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2402.04979 |