Embedded Dense Camera Trajectories in Multi-Video Image Mosaics by Geodesic Interpolation-based Reintegration

Dense registrations of huge image sets are still challenging due to exhaustive matchings and computationally expensive optimisations. Moreover, the resultant image mosaics often suffer from structural errors such as drift. Here, we propose a novel algorithm to generate global large-scale registrations from thousands of images extracted from multiple videos to derive high-resolution image mosaics which include full frame rate camera trajectories. Our algorithm does not require any initialisations and ensures the effective integration of all available image data by combining efficient and highly parallelised key-frame and loop-closure mechanisms with a novel geodesic interpolation-based reintegration strategy. As a consequence, global refinement can be done in a fraction of iterations compared to traditional optimisation strategies, while effectively avoiding drift and convergence towards inappropriate solutions. We compared our registration strategy with state-of-the-art algorithms and quantitative evaluations revealed millimetre spatial and high angular accuracy. Applicability is demonstrated by registering more than 110,000 frames from multiple scan recordings and provide dense camera trajectories in a globally referenced coordinate system as used for drone-based mappings, ecological studies, object tracking and land surveys.