Extraction of 3D freeform surfaces as visual landmarks for real-time tracking
This work presents a system for the generation of a free-form surface model from video sequences. Although any single centered camera can be applied in the proposed system the approach is demonstrated using fish-eye lenses because of their good properties for tracking. The system is designed to function automatically and to be flexible with respect to size and shape of the reconstructed scene. To minimize geometric assumptions a statistic fusion of dense depth maps is utilized. Special attention is payed to the necessary rectification of the spherical images and the resulting iso-disparity surfaces, which can be exploited in the fusion approach. Before dense depth estimation can be performed the cameras pose parameters are extracted by means of a structure-from-motion (SfM) scheme. In this respect automation of the system is achieved by thorough decision model based on robust statistics and error propagation of projective measurement uncertainties. This leads to a scene-independent set of only a few parameters. All system components are formulated in a general way, making it possible to cope with any single centered projection model, in particular with spherical cameras. In using wide field-of-view cameras the presented system is able to reliably retrieve poses and consistently reconstruct large scenes. A textured triangle mesh constructed on basis of the scenes reconstructed depth, makes the systems results suitable to function as reference models in a GPU driven analysis-by-synthesis framework for real-time tracking.
rectification structure-from-motion robust statistics omnidirectional vision visual scene reconstruction