Neural Network 2D to 3D Conversion System
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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 the complexity of mathematical models used for depth estimation.
Innovation Solution
A neural network-based system that processes two-dimensional video content to generate three-dimensional content by analyzing image characteristics such as contrast, sharpness, chrominance, and texture, using a depth map to create stereo-pair images, allowing for fully automated and high-quality 2D to 3D conversion, capable of handling large datasets efficiently and reducing power consumption.
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 results in sub-optimal quality
Solution Approach 1:
The patent replaces manual mechanical processes with optical systems. Specifically, it uses a dual-camera setup with fixed baselines and controlled parallaxes to automatically capture stereoscopic pairs, eliminating the need for manual depth estimation while maintaining high quality 3D content generation
Solution Approach 2:
The patent introduces an intermediary processing system that automatically matches features between left and right camera images, computes depth information, and generates 3D content. This intermediary system bridges the gap between simple 2D capture and complex 3D generation without requiring manual intervention
2Measurement precision
If complex mathematical models are used for depth estimation in glasses-free 3D displays, then depth accuracy may improve, but the complexity of the system increases
Solution Approach 1:
The patent changes the parameters of the optical system itself (camera baseline distance, focal length, lens aperture) to encode depth information directly in the captured images. This physical parameter adjustment replaces complex computational models with simpler processing based on controlled optical geometry
Solution Approach 2:
The patent performs preliminary depth encoding during the image capture phase by using fixed camera baselines and controlled parallaxes. This preliminary action embeds depth information in the captured images, eliminating the need for complex real-time mathematical models during processing
3Adaptability or versatility
If more views are presented in glasses-free 3D displays, then the immersive experience improves, but the optical limitations and crosstalk increase
Solution Approach 1:
The patent segments the viewing experience into multiple discrete angular views, each captured by a separate camera in the array. This segmentation allows each view to be optimized independently, presenting only the necessary angular information to reduce crosstalk while maintaining immersive multi-view capability
Solution Approach 2:
The patent creates a universal display system that can present multiple views simultaneously to multiple viewers at different positions. The system uses a single display surface that universally serves all viewing angles, eliminating the need for separate displays for each view and reducing optical interference
Data Source
AI summary
A three dimensional system including a marker mode.