KEYWORDS: 3D modeling, Data modeling, Depth maps, 3D acquisition, RGB color model, Neural networks, Data acquisition, Holographic displays, Computer generated holography, 3D image processing
We propose an advanced layering method of integrated dept-position map for real-world object. The depth map of far field object has not contain depth information and impossible to layering the far field objects. Therefore, the position map was rendered from generated high-quality 3D model used deep neural network, it is accurately layering for far field object. However, it has field loss of 3D model depending on the color density, when layering at near field. Therefore, by combining the depth map with the position map, the proposed integrated depth-position map was obtained for accurately layering in far and near field objects.
KEYWORDS: Cameras, Image acquisition, 3D modeling, 3D acquisition, 3D displays, Integral imaging, Image processing, Deep learning, 3D image processing, Digital cameras
In this report, we proposed an advanced integral imaging 3D display system using a simplified high-resolution light field image acquisition method. A simplified light field image acquisition method consists of a minimized number of cameras (three cameras placed along the vertical axis) to acquire the high-resolution perspectives of a full-parallax light field image. Since the number of cameras is minimized, the number of perspectives (3×N) and the specifications of the 3D integral imaging display unit (N×N elemental lenses) cannot be matched. It is possible to utilize the additional intermediate-view elemental image generation method in the vertical axis; however, the generation of the vertical viewpoints as many as the number of elemental lenses is a quite complex process and requires huge computation/long processing time. Therefore, in this case, we use a pre-trained deep learning model, in order to generate the intermediate information between the vertical viewpoints. Here, the corrected perspectives are inputted into a custom-trained deep learning model, and a deep learning model analyzes and renders the remaining intermediate viewpoints along the vertical axis, 3×N → N×N. The elemental image array is generated from the newly generated N×N perspectives via the pixel rearrangement method; finally, the full-parallax and natural-view 3D visualization of the real-world object is displayed on the integral imaging 3D display unit.
his report proposes a three-dimensional/two-dimensional switchable augmented-reality display system using a liquid crystalline lens array and an electrical polarizer. A depth camera that is connected to the proposed augmented-reality display system acquires the three-dimensional or two-dimensional information of the real objects. Here, the dual function liquid-crystalline lens array is switched its function according to the polarizing directions of an electrical polarizer. The proposed system's overall procedure is as follows: the depth camera captures the depth/color, or only color image according to the switcher of a polarizer, and the three-dimensional or two-dimensional images are displayed separately on the augmented-reality display system. It gives an opportunity that three-dimensional and two-dimensional modes can be switched automatically. In the two-dimensional mode, the captured color image of a real object is displayed directly. In the three-dimensional mode, the elemental image array is generated from the depth and color images and reconstructed as a three-dimensional image by the liquid-crystalline microlens array of a proposed augmented-reality display system. Even the proposed system cannot be implemented the real-time display in the three dimensional mode, the direction-inversed computation method generates the elemental image arrays of the real object within a possible short time.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.