Neural Network Lens Modeling for Automated 2D to 3D Conversion
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Solution Overview
Problem
Current methods for converting two-dimensional video content to three-dimensional content are labor-intensive, cumbersome, and often result in sub-optimal quality, limiting the immersive experience for viewers, especially in glasses-free 3D displays due to limitations in optics and complexity of mathematical models.
Innovation Solution
A neural network-based system that processes two-dimensional video content to generate high-quality three-dimensional images by analyzing image characteristics such as contrast, sharpness, and texture, and using depth maps to create stereo-pair images, allowing for fully automated conversion and rendering on various 3D display technologies, including glasses-based and glasses-free formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional methods are used to convert 2D video content to 3D content, then the conversion process can be completed, but the process is labor-intensive and cumbersome requiring significant manual intervention
Solution Approach 1:
The patent replaces manual mechanical processes with an optical system comprising a camera array, lens model, and depth map generation algorithm. The system automatically captures multiple 2D images from different angles and uses computational algorithms to generate depth maps and synthesize 3D content, eliminating the need for manual stereoscopic pair selection and processing.
Solution Approach 2:
The system performs self-service by automatically generating depth maps from captured images, selecting appropriate stereoscopic pairs based on depth information, and rendering 3D content without requiring external manual intervention. The lens model and depth map algorithm work together autonomously to complete the entire conversion pipeline.
2Manufacturing precision
If conventional conversion techniques are used, then 3D content can be generated, but the quality is sub-optimal with artifacts and limited depth perception
Solution Approach 1:
The patent segments the conversion process into distinct stages: depth map generation from individual images, stereoscopic pair selection based on depth criteria, and rendering with controlled disparity. This segmentation allows each stage to be optimized independently, improving overall quality by ensuring that only appropriate image pairs with suitable depth characteristics are selected and processed.
Solution Approach 2:
The system changes key parameters including depth map thresholds, disparity limits, and stereoscopic pair selection criteria to optimize quality. By adjusting these parameters, the system can control the amount of depth information extracted and the degree of horizontal displacement applied, thereby reducing artifacts and improving depth perception accuracy.
3Manufacturing precision
If complex mathematical models are used for glasses-free 3D displays, then depth perception can be enhanced, but the complexity of the system increases significantly
Solution Approach 1:
The patent introduces depth maps as an intermediary element that simplifies the conversion process. Instead of using complex mathematical models to directly compute 3D geometry from 2D images, the system first generates depth maps that encode depth information in a simplified format, which then guides the stereoscopic pair selection and rendering processes.
4Productivity
If manual intervention is used in the conversion process, then quality control can be maintained, but productivity decreases due to labor-intensive processes
Solution Approach 1:
The system implements feedback mechanisms where depth map quality is evaluated and used to adjust stereoscopic pair selection criteria. The algorithm continuously monitors depth information and adjusts processing parameters to maintain quality standards automatically, replacing manual quality control with intelligent feedback-driven adjustments.
Data Source
AI summary
A three dimensional system including lens modeling.