Towards a Scalable Architecture for Real-Time Volume Rendering

In this paper we present our research eff orts towards a scalable volume rendering architecture for the real-time visualization of dynamically changing high-resolution datasets. Using a linearly skewed memory interleaving we were able to develop a parallel data ow model that leads to local, fixed-bandwidth interconnections between processing elements. This parallel dataflow model diff ers from previous work in that it requires no global communication of data except at the pixel level. Using this data ow model we are developing Cube-4, an architecture that is scalable to very high performances and allows for modular and extensible hardware implementations.Engineering and Applied Science

Hardware-accelerated interactive data visualization for neuroscience in Python.

Large datasets are becoming more and more common in science, particularly in neuroscience where experimental techniques are rapidly evolving. Obtaining interpretable results from raw data can sometimes be done automatically; however, there are numerous situations where there is a need, at all processing stages, to visualize the data in an interactive way. This enables the scientist to gain intuition, discover unexpected patterns, and find guidance about subsequent analysis steps. Existing visualization tools mostly focus on static publication-quality figures and do not support interactive visualization of large datasets. While working on Python software for visualization of neurophysiological data, we developed techniques to leverage the computational power of modern graphics cards for high-performance interactive data visualization. We were able to achieve very high performance despite the interpreted and dynamic nature of Python, by using state-of-the-art, fast libraries such as NumPy, PyOpenGL, and PyTables. We present applications of these methods to visualization of neurophysiological data. We believe our tools will be useful in a broad range of domains, in neuroscience and beyond, where there is an increasing need for scalable and fast interactive visualization

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

Reconfigurable Mobile Multimedia Systems

This paper discusses reconfigurability issues in lowpower hand-held multimedia systems, with particular emphasis on energy conservation. We claim that a radical new approach has to be taken in order to fulfill the requirements - in terms of processing power and energy consumption - of future mobile applications. A reconfigurable systems-architecture in combination with a QoS driven operating system is introduced that can deal with the inherent dynamics of a mobile system. We present the preliminary results of studies we have done on reconfiguration in hand-held mobile computers: by having reconfigurable media streams, by using reconfigurable processing modules and by migrating functions

Conceptual design study for an advanced cab and visual system, volume 2

The performance, design, construction and testing requirements are defined for developing an advanced cab and visual system. The rotorcraft system integration simulator is composed of the advanced cab and visual system and the rotorcraft system motion generator, and is part of an existing simulation facility. User's applications for the simulator include rotorcraft design development, product improvement, threat assessment, and accident investigation

CNN-based Real-time Dense Face Reconstruction with Inverse-rendered Photo-realistic Face Images

With the powerfulness of convolution neural networks (CNN), CNN based face reconstruction has recently shown promising performance in reconstructing detailed face shape from 2D face images. The success of CNN-based methods relies on a large number of labeled data. The state-of-the-art synthesizes such data using a coarse morphable face model, which however has difficulty to generate detailed photo-realistic images of faces (with wrinkles). This paper presents a novel face data generation method. Specifically, we render a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we construct a fine-detailed face image dataset by transferring different scales of details from one image to another. We also construct a large number of video-type adjacent frame pairs by simulating the distribution of real video data. With these nicely constructed datasets, we propose a coarse-to-fine learning framework consisting of three convolutional networks. The networks are trained for real-time detailed 3D face reconstruction from monocular video as well as from a single image. Extensive experimental results demonstrate that our framework can produce high-quality reconstruction but with much less computation time compared to the state-of-the-art. Moreover, our method is robust to pose, expression and lighting due to the diversity of data.Comment: Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence, 201

Real-time optical micro-manipulation using optimized holograms generated on the GPU

Holographic optical tweezers allow the three dimensional, dynamic, multipoint manipulation of micron sized dielectric objects. Exploiting the massive parallel architecture of modern GPUs we can generate highly optimized holograms at video frame rate allowing the interactive micro-manipulation of 3D structures.Comment: 13 pages, 8 figure