OpenMM is a software toolkit for performing molecular simulations on a range of high performance computing architectures. It is based on a layered architecture: the lower layers function as a reusable library that can be invoked by any application, while the upper layers form a complete environment for running molecular simulations. The library API hides all hardware-specific dependencies and optimizations from the users and developers of simulation programs: they can be run without modification on any hardware on which the API has been implemented. The current implementations of OpenMM include support for graphics processing units using the OpenCL and CUDA frameworks. In addition, OpenMM was designed to be extensible, so new hardware architectures can be accommodated and new functionality (e.g., energy terms and integrators) can be easily added.
We present a new image-based algorithm for surface reconstruction of moving garment from multiple calibrated video cameras. Using a color-coded cloth texture, we reliably match circular features between different camera views. As surface model we use an a priori known triangle mesh. By identifying the mesh vertices with texture elements we obtain a coherent parameterization of the surface over time without further processing. Missing data points resulting from self-shadowing are plausibly interpolated by minimizing a thin-plate functional. The deforming geometry can be used for different graphics applications, e.g. for realistic retexturing. We show results for real garments demonstrating the accuracy of the recovered flexible
The ability to interpolate between images taken at different time and viewpoints directly in image space opens up new possiblities. The goal of our work is to create plausible in-between images in real time without the need for an intermediate 3D reconstruction. This enables us to also interpolate between images recorded with uncalibrated and unsynchronized cameras. In our approach we use a novel discontiniuity preserving image deformation model to robustly estimate dense correspondences based on local homographies. Once correspondences have been computed we are able to render plausible in-between images in real time while properly handling occlusions. We discuss the relation of our approach to human motion perception and other image interpolation techniques.
We present a method for image interpolation that is able to create high-quality, perceptually convincing transitions between recorded images. By implementing concepts derived from human vision, the problem of a physically correct image interpolation is relaxed to that of image interpolation which is perceived as visually correct by human observers. We find that it suffices to focus on exact edge correspondences, homogeneous regions and coherent motion to compute convincing results. A user study confirms the visual quality of the proposed image interpolation approach. We show how each aspect of our approach increases perceived quality of the result. We compare the results to other methods and assess achievable quality for different types of scenes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.