Object pose using coordinates from positional encoding with high-frequency components with Park
Object pose using coordinates from positional encoding with high-frequency components with Park
Leveraging Positional Encoding for Robust Multi-Reference-Based Object 6D Pose Estimation
arXiv paper abstract https://arxiv.org/abs/2401.16284
arXiv PDF paper https://arxiv.org/pdf/2401.16284.pdf
... estimating the pose of an object is a crucial task in computer vision ... two main deep learning approaches for this: geometric representation regression and iterative refinement.
However, these ... have ... limitations ... analyze these limitations and propose new strategies to overcome them.
To tackle the issue of blurry geometric representation, ... use positional encoding with high-frequency components for the object's 3D coordinates.
To address the local minimum problem in refinement methods, ... introduce a normalized image plane-based multi-reference refinement strategy that's independent of intrinsic matrix constraints.
... utilize adaptive instance normalization and a simple occlusion augmentation method to help ... model concentrate on the target object.
... demonstrate ... approach outperforms existing methods ...
Please like and share this post if you enjoyed it using the buttons at the bottom!
Stay up to date. Subscribe to my posts https://morrislee1234.wixsite.com/website/contact
Web site with my other posts by category https://morrislee1234.wixsite.com/website
