We present a general paradigm for dynamic 3D reconstruction from multiple independent and uncontrolled image sources having arbitrary temporal sampling density and distribution. Our graph-theoretic formulation models the Spatio-temporal relationships among our observations in terms of the joint estimation of their 3D geometry and its discrete Laplace operator. Towards this end, we define a tri-convex optimization framework that leverages the geometric properties and dependencies found among a Euclideanshape-space and the discrete Laplace operator describing its local and global topology. We present a reconstructability analysis, experiments on motion capture data and multi-view image datasets, as well as explore applications to geometry-based event segmentation and data association.
 In this paper, we consider the problem of estimating the spatiotemporal alignment between N unsynchronized video sequences of the same dynamic 3D scene, captured from distinct viewpoints. Unlike most existing methods, which work for N = 2 and rely on a computationally intensive search in the space of temporal alignments, we present a novel approach that reduces the problem for general N to the robust estimation of a single line in RN. This line captures all temporal relations between the sequences and can be computed without any prior knowledge of these relations. Considering that the spatial alignment is captured by the parameters of fundamental matrices, an iterative algorithm is used to refine simultaneously the parameters representing the temporal and spatial relations between the sequences. Experimental results with real-world and synthetic sequences show that our method can accurately align the videos even when they have large misalignments (e.g., hundreds of frames), when the problem is seemingly ambiguous (e.g., scenes with roughly periodic motion), and when accurate manual alignment is difficult (e.g., due to slow-moving objects). We present a novel algorithm for temporally synchronizing multiple videos capturing the same dynamic scene. Our algorithm relies on general image features and it does not require explicitly tracking any specific object, making it applicable to general scenes with complex motion. This is facilitated by our new trajectory filtering and matching schemes that correctly identifies matching pairs of trajectories (inliers) from a large set of potential candidate matches, of which many are outliers. We find globally optimal synchronization parameters by using a stable RANSAC-based optimization approach. For multi-video synchronization, the algorithm identifies an informative subset of video pairs which prevents the RANSAC algorithm from being biased by outliers. Experiments on two-camera and multi-camera synchronization demonstrate the performance of our algorithm.  Photo-sequencing is the problem of recovering the temporal order of a set of still images of a dynamic event, taken asynchronously by a set of uncalibrated cameras. Solving this problem is a first, crucial step for analyzing (or visualizing) the dynamic content of the scene captured by a large number of freely moving spectators. We propose a geometric based solution, followed by rank aggregation to the photo-sequencing problem. Our algorithm trades spatial certainty for temporal certainty. Whereas the previous solution proposed by [4] relies on two images taken from the same static camera to eliminate uncertainty in space, we drop the static-camera assumption and replace it with temporal information available from images taken from the same (moving) camera. Our method thus overcomes the limitation of the static-camera assumption, and scales much better with the duration of the event and the spread of cameras in space. We present successful results on challenging real data sets and large scale synthetic data (250 images).  We present an algorithm that synchronizes two short video sequences where an object undergoes ballistic motion against stationary scene points. The object’s motion and epipolar geometry are exploited to guide the algorithm to the correct synchronization in an iterative manner. Our algorithm accurately synchronizes videos recorded at different frame rates, and takes few iterations to converge to sub-frame accuracy. We use synthetic data to analyze our algorithm’s accuracy under the influence of noise. We demonstrate that it accurately synchronizes real video sequences, and evaluate its performance against manual synchronization. This paper presents a method of synchronizing video sequences that exploits the non-rigidity of sets of 3D point features (e.g., anatomical joint locations) within the scene. The theory is developed for homography, perspective and affine projection models within a unified rank constraint framework that is computationally cheap. An efficient method is then presented that recovers potential frame correspondences, estimates possible synchronization parameters via the Hough transform and refines these parameters using non-linear optimization methods in order to recover synchronization to sub-frame accuracy, even for sequences of unknown and different frame rates. The method is evaluated quantitatively using synthetic data and demonstrated qualitatively on several real sequences. We present a novel method for automatically synchronizing two video sequences of the same event. Unlike previously proposed methods, we do not put any restrictive constraints on the scene nor on the camera motions: our method can deal with independently moving cameras, wide baseline conditions, and general 3D scenes. It starts from five point correspondences throughout the video sequences, that are provided using wide baseline matching and tracking techniques. It is efficient, in that it can be implemented in a non-combinatorial way. The feasibility of the method is demonstrated by preliminary experimental results. In this work, a method that synchronizes two video sequences is proposed. Unlike previous methods, which require the existence of correspondences between features tracked in the two sequences, and or that the cameras are static or jointly moving, the proposed approach does not impose any of these constraints. It works when the cameras move independently, even if different features are tracked in the two sequences. The assumptions underlying the proposed strategy are that the intrinsic parameters of the cameras are known and that two rigid objects, with independent motions on the scene, are visible in both sequences. The relative motion between these objects is used as clue for the synchronization. The extrinsic parameters of the cameras are assumed to be unknown. A new synchronization algorithm for static or jointly moving cameras that see (possibly) different parts of a common rigidly moving object is also proposed. Proof-of-concept experiments that illustrate the performance of these methods are presented, as well as a comparison with a state-of-the-art approach.