Generalized Exemplar-Based Full Pose Estimation from 2D Images without Correspondences
This paper addresses the problem of full pose estimation of objects in 2D images, using registered 2D examples as training data. We present a general formulation of the problem, which departs from traditional approaches by not focusing on one specific type of image features. The proposed algorithm avoids relying on specific model-to-scene correspondences, allowing using similar-looking and generally unmatchable features. We effectively demonstrate this capability by applying the method to edge segments. Our algorithm uses successive histogram-based and probabilistic evaluations, which ultimately recover a complete description of the probability distribution of the pose of the object, in the 6 degree-of-freedom 3D pose space, thereby accounting for the inherent ambiguities in the 2D input data. Furthermore, we propose, in a rigorous framework, an efficient procedure for fusing multiple sources of evidence, such as multiple registered 2D views of the same scene. The proposed method is evaluated qualitatively and quantitatively on synthetic and real test images. It shows promising results under challenging conditions, including occlusions and heavy clutter, while being capable of handling objects with little texture and detail.