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 …
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