We present an integrated camera motion design and path generation system for building volume data animations. Creating animations is an essential task in presenting complex scientific visualizations. Existing visualization systems use an established animation function based on keyframes selected by the user. This approach is limited in providing the optimal in-between views of the data. Alternatively, computer graphics and virtual reality camera motion planning is frequently focused on collision free movement in a virtual walkthrough. For semi-transparent, fuzzy, or blobby volume data the collision free objective becomes insufficient. Here, we provide a set of essential criteria focused on computing camera paths to establish effective animations of volume data. Our dynamic multi-criteria solver coupled with a force-directed routing algorithm enables rapid generation of camera paths. Once users review the resulting animation and evaluate the camera motion, they are able to determine how each criterion impacts path generation. In this paper, we demonstrate how incorporating this animation approach with an interactive volume visualization system reduces the effort in creating context-aware and coherent animations. This frees the user to focus on visualization tasks with the objective of gaining additional insight from the volume data.

Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.

Images that seamlessly combine views at different levels of detail are appealing. However, creating such multiscale images is not a trivial task, and most such illustrations are handcrafted by skilled artists. This paper presents a framework for direct multiscale rendering of geometric and volumetric models. The basis of our approach is a set of non-linearly bent camera rays that smoothly cast through multiple scales. We show that by properly setting up a sequence of conventional pinhole cameras to capture features of interest at different scales, along with image masks specifying the regions of interest for each scale on the projection plane, our rendering framework can generate non-linear sampling rays that smoothly project objects in a scene at multiple levels of detail onto a single image. We address two important issues with non-linear camera projection. First, our streamline-based ray generation algorithm avoids undesired camera ray intersections, which often result in unexpected images. Second, in order to maintain camera ray coherence and preserve aesthetic quality, we create an interpolated 3D field that defines the contribution of each pinhole camera for determining ray orientations. The resulting multiscale camera has three main applications: (1) presenting hierarchical structure in a compact and continuous manner, (2) achieving focus+context visualization, and (3) creating fascinating and artistic images.