Search for: All records

The latest light-field displays have improved greatly, but continue to be based on the approximate pinhole model. For every frame, our real-time technique evaluates a full optical model, and then renders an image predistorted at the sub-pixel level to the current pixel-to-eye light flow, reducing cross-talk and increasing viewing angle. 

ABSTRACT Many microscopic images and simulations of cells give results in different kinds of formats, making it difficult for people lacking computational skills to visualize and interact with them. Minecraft—known for its three-dimensional, open-world, voxel-based environment—offers a unique solution by allowing the direct insertion of voxel-based cellular structures from light microscopy and simulations into its worlds without modification. This integration enables Minecraft players to explore the ultrastructure of cells in a highly immersive and interactive environment. Here, we demonstrate several workflows that can convert images and simulation results into Minecraft worlds. Using the workflows, students can easily import and interact with a variety of cellular content, including bacteria, yeast, and cancer cells. This approach not only opens new avenues for science education but also demonstrates the potential of combining scientific visualization with interactive gaming platforms for facilitating research and improving appreciation of cellular structure for a broad audience. 

Current light‐field displays increase resolution and reduce cross‐talk with head tracking, despite using simple lens models. With a more complete model, our real‐time technique uses GPUs to analyze the current frame's light flow at subpixel precision, and to render a matching image that further improves resolution and cross‐talk. 

Emerging display usage scenarios require head tracking both at short (< 1m) and modest (< 3m) ranges. Yet it is difficult to find low-cost, unobtrusive tracking solutions that remain accurate across this range. By combining multiple head tracking solutions, we can mitigate the weaknesses of one solution with the strengths of another and improve head tracking overall. We built such a combination of two widely available and low-cost trackers, a Tobii Eye Tracker and a Kinect. The resulting system is more effective than Kinect at short range, and than the Tobii at a more distant range. In this paper, we discuss how we accomplish this sensor fusion and compare our combined system to an existing mechanical tracker to evaluate its accuracy across its combined range.