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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.14577 |
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| _version_ | 1866914954747052032 |
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| author | Huang, Sining Song, Yukun Kang, Yixiao Yu, Chang |
| author_facet | Huang, Sining Song, Yukun Kang, Yixiao Yu, Chang |
| contents | In the field of spatial computing, one of the most essential tasks is the pose estimation of 3D objects. While rigid transformations of arbitrary 3D objects are relatively hard to detect due to varying environment introducing factors like insufficient lighting or even occlusion, objects with pre-defined shapes are often easy to track, leveraging geometric constraints. Curved images, with flexible dimensions but a confined shape, are essential shapes often targeted in 3D tracking. Traditionally, proprietary algorithms often require specific curvature measures as the input along with the original flattened images to enable pose estimation for a single image target. In this paper, we propose a pipeline that can detect several logo images simultaneously and only requires the original images as the input, unlocking more effects in downstream fields such as Augmented Reality (AR). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14577 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way Huang, Sining Song, Yukun Kang, Yixiao Yu, Chang Computer Vision and Pattern Recognition In the field of spatial computing, one of the most essential tasks is the pose estimation of 3D objects. While rigid transformations of arbitrary 3D objects are relatively hard to detect due to varying environment introducing factors like insufficient lighting or even occlusion, objects with pre-defined shapes are often easy to track, leveraging geometric constraints. Curved images, with flexible dimensions but a confined shape, are essential shapes often targeted in 3D tracking. Traditionally, proprietary algorithms often require specific curvature measures as the input along with the original flattened images to enable pose estimation for a single image target. In this paper, we propose a pipeline that can detect several logo images simultaneously and only requires the original images as the input, unlocking more effects in downstream fields such as Augmented Reality (AR). |
| title | AR Overlay: Training Image Pose Estimation on Curved Surface in a Synthetic Way |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.14577 |