The simulation of complex events is a challenging task and often requires careful selection of simulation parameters. With the availability of vast computation resources, it has become possible to run several alternative parameter settings or simulation models in parallel, creating an 'ensemble' of possible outcomes for a given event of interest. Recently, the visual analysis of such ensemble data has repeatedly come up as one of the most important new areas of visualization and it is expected to have a wide impact on the field of visualization in the next few years. The main challenge is to develop expressive visualizations of properties of this set of solutions, the ensemble, to support scientists in this challenging parameter-space exploration task. This paper presents and explores future challenges for ensemble visualization.

A novel method to detect and correct inaccuracies in a set of unconstrained dense correspondences between two images is presented. Starting with a robust, general-purpose dense correspondence algorithm, an initial pose estimate and dense 3D scene reconstruction are obtained and bundle-adjusted. Reprojection errors are then computed for each correspondence pair, which is used as a metric to distinguish high and low-error correspondences. An affine neighborhood-based coarse-to-fine iterative search algorithm is then applied only on the high-error correspondences to correct their positions. Such an error detection and correction mechanism is novel for unconstrained dense correspondences, for example not obtained through epipolar geometry-based guided matching. Results indicate that correspondences in regions with issues such as occlusions, repetitive patterns and moving objects can be identified and corrected, such that a more accurate set of dense correspondences results from the feedback-based process, as proven by more accurate pose and structure estimates.

As computational capabilities increasingly outpace disk speeds on leading supercomputers, scientists will, in turn, be increasingly unable to save their simulation data at its native resolution. One solution to this problem is to compress these data sets as they are generated and visualize the compressed results afterwards. We explore this approach, specifically subsampling velocity data and the resulting errors for particle advection-based flow visualization. We compare three techniques: random selection of subsamples, selection at regular locations corresponding to multi-resolution reduction, and introduce a novel technique for informed selection of subsamples. Furthermore, we explore an adaptive system which exchanges the subsampling budget over parallel tasks, to ensure that subsampling occurs at the highest rate in the areas that need it most. We perform supercomputing runs to measure the effectiveness of the selection and adaptation techniques. Overall, we find that adaptation is very effective, and, among selection techniques, our informed selection provides the most accurate results, followed by the multi-resolution selection, and with the worst accuracy coming from random subsamples.

We present the general idea of a computer vision structure-from-motion framework that makes use of sensor fusion to provide very accurate and efficient multi-view reconstruction results that can capture internal geometry. Given the increased ubiquity and cost-effectiveness of embedding sensors, such as positional sensors, into objects, it has become feasible to fuse such sensor data and camera-acquired data to vastly improve reconstruction quality and enable a number of novel applications for structure-from-motion. Application areas, which require very high accuracy, include medicine, robotics, security, and additive manufacturing (3D printing). Specific examples and initial results are discussed, followed by a discussion on proposed future work.

Fluid mechanics considers two frames of reference for an observerwatching a flow field: Eulerian and Lagrangian. The former isthe frame of reference traditionally used for flow analysis, and involvesextracting particle trajectories based on a vector field. Withthis work, we explore the opportunities that arise when consideringthese trajectories from the Lagrangian frame of reference. Specifically,we consider a form where flows are extracted in situ and thenused for subsequent post hoc analysis. We believe this alternate,Lagrangian-based form will be increasingly useful, because the Eulerianframe of reference is sensitive to temporal frequency, andarchitectural trends are causing temporal frequency to drop rapidlyon modern supercomputers. We support our viewpoint by running aseries of experiments, which demonstrate the Lagrangian form canbe more accurate, require less I/O, and be faster when compared totraditional advection.

We present the general idea of using common tools from the field of scientific visualization to aid in the design, implementation and testing of computer vision algorithms, as a complementary and educational component to purely mathematics-based algorithms and results. The interaction between these two broad disciplines has been basically non-existent in the literature, and through initial work we have been able to show the benefits of merging visualization techniques into vision for analyzing patterns in computed parameters. Specific examples and initial results are discussed, such as scalar field-based renderings for scene reconstruction uncertainty and sensitivity analysis as well as feature tracking summaries, followed by a discussion on proposed future work.

This paper discusses the constraints imposed by the path of a moving camera in multi-view sequential scene reconstruction scenarios such as in aerial video, which allow for an efficient detection and correction of inaccuracies in the feature tracking and structure computation processes. The main insight is that for short, planar segments of a continuous camera trajectory, parallax movement corresponding to a viewed scene point should ideally form a scaled and translated version of this trajectory when projected onto a parallel plane. Two inter-camera and intra-camera constraints arise, which create a prediction of where all feature tracks should be located given the consensus information of all accurate tracks and cameras, which allows for the detection and correction of inaccurate feature tracks, as well as a very simple update of scene structure. This procedure differs from classical approaches such as factorization and RANSAC. In both aerial video and turntable sequences, the use of such constraints was proven to correct outlier tracks, detect and correct tracking drift, allow for a simple updating of scene structure, and improve bundle adjustment convergence.

The angular error-based triangulation method and the par- allax path method are both high-performance methods for large-scale multi-view sequential reconstruction that can be parallelized on the GPU. We map parallax paths to the GPU and test its performance and accuracy as a triangulation method for the rst time. To this end, we compare it with the angular method on the GPU for both performance and accuracy. Furthermore, we improve the recovery of path scales and perform more extensive analysis and testing compared with the original parallax paths method. Although parallax paths requires sequential and piecewise-planar camera positions, in such scenarios, we can achieve a speedup of up to 14x over angular triangulation, while maintaining comparable accuracy.