Flight Dynamics-based Recovery of a UAV Trajectory using Ground Cameras

We propose a new method to estimate the 6-dof trajectory of a flying object such as a quadrotor UAV within a 3D airspace monitored using multiple fixed ground cameras. It is based on a new structure from motion formulation for the 3D reconstruction of a single moving point with known motion dynamics. Our main contribution is a new bundle adjustment procedure which in addition to optimizing the camera poses, regularizes the point trajectory using a prior based on motion dynamics (or specifically flight dynamics). Furthermore, we can infer the underlying control input sent to the UAV's autopilot that determined its flight trajectory. Our method requires neither perfect single-view tracking nor appearance matching across views. For robustness, we allow the tracker to generate multiple detections per frame in each video. The true detections and the data association across videos is estimated using robust multi-view triangulation and subsequently refined during our bundle adjustment procedure. Quantitative evaluation on simulated data and experiments on real videos from indoor and outdoor scenes demonstrates the effectiveness of our method

Reconstruction of surface of revolution from multiple uncalibrated views: a bundle-adjustment approach

This paper addresses the problem of recovering the 3D shape of a surface of revolution from multiple uncalibrated perspective views. In previous work, we have exploited the invariant properties of the surface of revolution and its silhouette to recover the contour generator and hence the meridian of the surface of revolution from a single uncalibrated view. However, there exists one degree of freedom in the reconstruction which corresponds to the unknown orientation of the revolution axis of the surface of revolution. In this paper, such an ambiguity is removed by estimating the horizon, again, using the image invariants associated with the surface of revolution. A bundle-adjustment approach is then proposed to provide an optimal estimate of the meridian when multiple uncalibrated views of the same surface of revolution are available. Experimental results on real images are presented, which demonstrate the feasibility of the approach.postprintThe 6th Asian Conference on Computer Vision (ACCV 2004), Jeju, Korea, 27-30 January 2004. In Proceedings of the 6th Asian Conference on Computer Vision, 2004, v. 1, p. 378-38

Finding Optimal Views for 3D Face Shape Modeling

A fundamental problem in multi-view 3D face modeling is the determination of the set of optimal views (poses) required for accurate 3D shape estimation of a generic face. There is no analytical solution to this problem, instead (partial) solutions require (near) exhaustive combinatorial search, hence the inherent computational difficulty of this task. We build on our previous modeling framework [Silhouette-based 3D face shape recovery, Model-based 3D face capture using shape-from-silhouettes] which uses an efficient contour-based silhouette method and extend it by aggressive pruning of the view-sphere with view clustering and various imaging constraints. A multi-view optimization search is performed using both model-based (eigenheads) and data-driven (visual hull) methods, yielding comparable best views. These constitute the first reported set of optimal views for 3D face shape capture and provide useful empirical guidelines for the design of 3D face recognition systems.Engineering and Applied Science