Daisuke Kotake

A hybrid and linear registration method utilizing inclination constraint

This paper describes a new hybrid vision-based registration method with an inclination sensor. In the method, a camera is tracked by solving linear equations under inclination constraint. Linear operations are faster than the nonlinear optimization process, but the output does not usually satisfy the orthonormality constraint. On the contrary, the proposed method calculates the camera position and azimuth directly, thus the result satisfies the orthonormality constraint. Many hybrid approaches using inertia sensors have been proposed for AR/MR. However, such methods still depend on vision-based methods in initialization processes. On the other hand, the proposed method is totally hybrid in that the inclination measured by the sensor is always incorporated in pose calculation process as well as vision information. The method can be used in initialization process of conventional hybrid methods as well as it can be used as an independent registration method. The proposed method can be applied to not only inside-out-style camera tracking but also outside-in-style object tracking.

A Fast Initialization Method for Edge-based Registration Using an Inclination Constraint

We propose a hybrid camera pose estimation method using an inclination sensor value and correspondence-free line segments. In this method, possible azimuths of the camera pose are hypothesized by a voting method under an inclination constraint. Then some camera positions for each possible azimuth are calculated based on the detected line segments that affirmatively voted for the azimuth. Finally, the most consistent one is selected as the camera pose out of the multiple sets of the camera positions and azimuths. Unlike many other tracking methods, our method does not use past information but rather estimates the camera pose using only present information. This feature is useful for an initialization measure of registration in augmented reality (AR) systems. This paper describes the details of the method and shows its effectiveness with experiments in which the method is actually used in an AR application.

Data compression and hardware implementation of ray-space rendering for interactive augmented virtuality

This article describes approaches to solve two drawbacks of ray-space representation: the large amount of data necessary to represent an object and the massive use of CPU to render an image. Ray-space representation, an image-based rendering technique, is used in our interactive augmented virtuality system. We developed a compression method optimized for ray-space data and a hardware architecture to render images from ray-space data. The compression method uses a hybrid combination of motion-compensated prediction, discrete cosine transform, and vector quantization. The proposed method compresses the data while assuring random and fast access to the decoded data. The dedicated hardware architecture for consumer PCs to interactively render photorealistic images using ray-space representation is also described. This hardware architecture is used to efficiently transfer the processing load from the CPU to the hardware. These two improvements help to use PCs that do not have much memory and CPU resources in applications such as an interactive virtual museum, in which the scenes are generated from both geometric model data and ray-space data.