Yunzhe Xiao
ABSTRACT
Estimating transformation parameters between 3D point sets is crucial for various motion-based 3D vision tasks, such as point cloud registration, pose estimation, 3D object recognition, and tracking. Traditional methods rely on optimizing parameters from a subset of physical point pairs with hard correspondences. However, these approaches face challenges in complex scenes with sparse or partially overlapping point sets, lacking sufficient corresponding points. In this paper, we propose a novel transformation estimation approach based on soft dissimilarity, a metric that quantifies point-to-point correlation using spatial distance. Our method leverages soft dissimilarity to obtain ample corresponding point pairs for each raw point, improving the estimation process. Extensive experiments demonstrate that our method achieves superior accuracy and robustness compared to traditional correspondence-based transformation estimation methods across various scenarios.
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Index Terms
- Fitting Two Point Sets with Soft Dissimilarity
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