Practical system for generating digital mixed reality video holograms.

We propose a practical system that can effectively mix the depth data of real and virtual objects by using a Z buffer and can quickly generate digital mixed reality video holograms by using multiple graphic processing units (GPUs). In an experiment, we verify that real objects and virtual objects can be merged naturally in free viewing angles, and the occlusion problem is well handled. Furthermore, we demonstrate that the proposed system can generate mixed reality video holograms at 7.6 frames per second. Finally, the system performance is objectively verified by users' subjective evaluations.

Fast generation of a high-quality computer-generated hologram using a scalable and flexible PC cluster.

In order to efficiently generate a high-quality computer-generated hologram (HQ-CGH), which requires that both a three-dimensional object image and its computer-generated hologram (CGH) are in high-definition resolution, we implement a fast CGH generation system using a scalable and flexible personal computer (PC) cluster. From experimental results obtained in generating a HQ-CGH with a CGH resolution of 1536×1536 and 2,155,898 light sources using a PC cluster comprising a server PC and nine client PCs, it is verified that the proposed system is approximately 4.7 times faster than a single PC with two high-performance GPUs.

Optimal local searching for fast and robust textureless 3D object tracking in highly cluttered backgrounds.

Edge-based tracking is a fast and plausible approach for textureless 3D object tracking, but its robustness is still very challenging in highly cluttered backgrounds due to numerous local minima. To overcome this problem, we propose a novel method for fast and robust textureless 3D object tracking in highly cluttered backgrounds. The proposed method is based on optimal local searching of 3D-2D correspondences between a known 3D object model and 2D scene edges in an image with heavy background clutter. In our searching scheme, searching regions are partitioned into three levels (interior, contour, and exterior) with respect to the previous object region, and confident searching directions are determined by evaluating candidates of correspondences on their region levels; thus, the correspondences are searched among likely candidates in only the confident directions instead of searching through all candidates. To ensure the confident searching direction, we also adopt the region appearance, which is efficiently modeled on a newly defined local space (called a searching bundle). Experimental results and performance evaluations demonstrate that our method fully supports fast and robust textureless 3D object tracking even in highly cluttered backgrounds.

Fast model-based multispectral imaging using nonnegative principal component analysis.

Estimation of the spectral reflectance of a scene is a critical problem in image processing and computer vision applications. Model-based multispectral imaging, one of the spectral reflectance estimation methods, can effectively reconstruct the full spectrum using a small number of camera shots. However, it is based on iterative optimization and, thus, is computationally too intensive. In this Letter, we modify the iterative optimization problem to a closed-form problem using nonnegative principal component analysis. The proposed method can substantially reduce the computational cost while maintaining the accuracy.