Fig. 1. The commercial 16 camera system, an equirectangular depth map, and final color rendering produced from our system. Designing a fully integrated 360 • video camera supporting 6DoF head motion parallax requires overcoming many technical hurdles, including camera placement, optical design, sensor resolution, system calibration, real-time video capture, depth reconstruction, and real-time novel view synthesis. While there is a large body of work describing various system components, such as multi-view depth estimation, our paper is the first to describe a complete, reproducible system that considers the challenges arising when designing, building, and deploying a full end-to-end 6DoF video camera and playback environment. Our system includes a computational imaging software pipeline supporting online markerless calibration, high-quality reconstruction, and real-time streaming and rendering. Most of our exposition is based on a professional 16-camera configuration, which will be commercially available to film producers. However, our software pipeline is generic and can handle a variety of camera geometries and configurations. The entire calibration and reconstruction software pipeline along with example datasets is open sourced to encourage follow-up research in high-quality 6DoF video reconstruction and rendering 1 .

We have developed a system to translate and interpret printed documents (such as periodicals) using a commercially available mobile device (e.g., a mobile telephone) with an embedded camera. The system comprises an automatic layout analysis tool along with an Optical Character Recognition (OCR) engine and a translation engine. The translation engine combines Rule-Based Machine Translation (RBMT) software, a list of context-sensitive words/phrases, and an encyclopedia for a list of words. We implemented the proposed system on a Nokia N900 smartphone for translating newspaper articles from Spanish to English. Our tests show that the accuracy and speed of the system is mostly influenced by the accuracy and speed of the OCR method used. Our application requires 19.43 MB of memory and no more than 28 MB of dynamic memory. We show the ability to complete the process for roughly 410 words in as little as 65 seconds for high accuracy or 17 seconds for high speed, on average. The energy consumption in both cases is minimal.

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