Hierarchical Integration of Local 3D Features for Probabilistic Pose Recovery
Renaud J. Detry, Justus Piater · Open Repository and Bibliography (University of Liège) · 2007
This paper presents a 3D object representation framework. We develop a hierarchical model based on probabilistic correspondences and probabilistic relations between 3D visual features. Features at the bottom of the hierarchy are bound to local observations. Pairs of features that present strong geometric correlation are iteratively grouped into higher-level meta-features that encode probabilistic relative spatial relationships between their children. The model is instantiated by propagating evidence up and down the hierarchy using a Belief Propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge. We demonstrate how to use our framework to estimate the pose of a known object in an unknown scene, and provide a quantitative performance evaluation on synthetic data.