Heterogeneous Multi-View Information Fusion: Review of 3-D Reconstruction Methods and a New Registration with Uncertainty Modeling

Abstract

We consider a multisensor network fusion framework for 3-D data registration using inertial planes, the underlying geometric relations, and transformation model uncertainties. We present a comprehensive review of 3-D reconstruction methods and registration techniques in terms of the underlying geometric relations and associated uncertainties in the registered images. The 3-D data registration and the scene reconstruction task using a set of multiview images are an essential goal of structure-from-motion algorithms that still remains challenging for many applications, such as surveillance, human motion and behavior modeling, virtual-reality, smart-rooms, health-care, teleconferencing, games, human–robot interaction, medical imaging, and scene understanding. We propose a framework to incorporate measurement uncertainties in the registered imagery, which is a critical issue to ensure the robustness of these applications but is often not addressed. In our test bed environment, a network of sensors is used where each physical node consists of a coupled camera and associated inertial sensor (IS)/inertial measurement unit. Each camera-IS node can be considered as a hybrid sensor or fusion-based virtual camera. The 3-D scene information is registered onto a set of virtual planes defined by the IS. The virtual registrations are based on using the homography calculated from 3-D orientation data provided by the IS. The uncertainty associated with each 3-D point projected onto the virtual planes is modeled using statistical geometry methods. Experimental results demonstrate the feasibility and effectiveness of the proposed approach for multiview reconstruction with sensor fusion.