According to Gartner's 2013 Hype Cycle for Emerging Technologies, Augmented Reality will reach its Plateu of Productivity before Wearable User Interfaces. In this work, device and user-perspective rendering are compared regarding their applicability to AR-based solutions for Google Glass. The conducted experiment measured and evaluated the advantages and drawbacks on each method and also got positive and negative feedbacks given by users. The tests showed that users preferred the device-perspective approach.

This demonstration allows visitors to use AR applications for monocular optical see-through head-mounted displays with two forms of visualization. One is the device-perspective approach, in which the user sees the virtual content registered with the camera image at the display. The other is the user-perspective method, in which the display is used as a de facto optical see-through device and the virtual content is registered with the real world.

This demo presents an AR application that helps the user to solve a jigsaw puzzle that consists of non-textured pieces with a discriminative shape. The pieces are detected, their poses are estimated and the ones that are correctly assembled are highlighted. In order to detect the pieces, the Depth-Assisted Rectification of Contours (DARC) method is used, which performs detection and pose estimation of texture-less planar objects using an RGB-D camera.

We introduce a parallel algorithm to solve approximate and exact nearest neighbor queries on the GPU, exploiting its massively parallel processing power. Both data structure construction and nearest neighbor queries are performed on the GPU, avoiding memory copies from system memory to device memory. This algorithm achieves real-time performance, enabling its usage in dynamic scenarios, by minimizing the sorting comparisons needed for a large K value. The underlying data structure for spatial subdivision handles 3D points and is based on grid spatial hashing. Users can specify the grid size interactively. Comparisons were done with other nearest neighbor algorithms implemented on both CPU and GPU. Our approach clearly surpasses CPU implementations regarding processing time, while it presents a competitive solution to GPU ones. Real-time results were obtained with ANN searches (K = 10) for data sets up to 163K points and the potential of our algorithm is demonstrated through a point-based rendering application.

Current GPU computational power enables the execution of complex and parallel algorithms, such as Ray Tracing techniques supported by kD-trees for 3D scene rendering in real time. This work describes in detail the study and implementation of five different kD-Tree traversal algorithms using the parallel framework NVIDIA Compute Unified Device Architecture (CUDA), in order to point their pros and cons regarding adaptation capability to the chosen architecture. In addition, a new algorithm is proposed by the authors based on this analysis, aiming performance improvement. A performance analysis of the implemented techniques demonstrates that two of these algorithms, once adequately adapted to CUDA architecture, are capable of reaching speedup gains up to 15x when compared to former CPU implementations and up to 4x in comparison to existing and optimized parallel ones. As a consequence, interactive frame rates are possible for scenes with 1376x768 pixels of resolution and 1 million primitives.

Single processor technology has been evolving across last decades, but due to physical limitations of chip manufacturing process, the industry is pursuing alternatives to sustain computational power growth,including the creation of multi-core systems. Parallel computing targets problems that are scalable and possibly distributed, dividing the problem into smaller pieces. This approach may be explored to satisfy real-time constraints required by real time physics simulation. This work introduces an optimal solution to implement accurate simulation of deformable bodies in real time. This is accomplished through the use of a meshless simulation technique, known as Point Based Animation, implemented using a parallel framework:the NVIDIA Compute Unified Device Architecture(CUDA). Our implementation showed significative improvements on performance in comparison to the former CPU one: impressive speedups around 20times could be achieved in the simulation of deformable bodies with 575 physical elements (phyxels) and53,504 surface elements (surfels).

Recent years have promoted important evolution in remote sensing, specially taking into consideration quality improvements on satellites that are now capable of obtaining more detailed and accurate observation and producing stereoscopic views. Following market laws, once the level of detail increases the associated costs also rise. In order to take advantage of modest dataset acquisition costs the OpenStereo library (a free, open source, multiplatform, flexible and robust library for stereoscopy) is applied with the purpose of converting satellite images and similar types of geodata acquirers into stereoscopic views.