Recovering the 3D structure of tubular objects from stereo silhouettes

摘要

Although silhouette-based image understanding is attractive from an engineering viewpoint, recovering 3D shape from a single stereo pair of silhouette images of a generic multiple-object scene is a highly underconstrained problem. With respect to a gray-level-based approach, the ambiguities in stereo matching and the loss of data due to mutual visual occlusions are even more severe. These problems are alleviated when the observed objects can be assumed to belong to some restricted class. In this paper we consider the case of almost vertical tubular objects (AVTOs), i.e. generalized cylinders with some restrictions on their axis' shape and pose relative to the stereo pair. This restriction, together with the assumption that the scene must be explained with the minimum number of objects consistent with the observations, allows one to devise an effective reconstruction algorithm. The object shape/location parameters are estimated by recursive least-squares (Kalman) filtering. Constrained blind tracking is performed on the occluded sections by feeding the filters with the most likely parameter values compatible with the constraints induced by the observed images. The case of AVTOs with circular cross-section is analyzed in some detail, with examples taken from an actual implementation of the algorithm in the field of agricultural automation.