Automated 2D to 3D Conversion via Neural Network Depth Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for converting two-dimensional (2D) video content to three-dimensional (3D) 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 used for depth estimation.
Innovation Solution
A neural network-based system that processes 2D video content to generate 3D images by analyzing motion vectors, image characteristics such as contrast, sharpness, and texture, and using depth maps to create stereo-pair images, allowing for fully automated conversion with improved depth estimation and rendering techniques, including non-linear mappings and depth re-profiling, to enhance the 3D pop-out effect.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional 2D to 3D conversion techniques are used, then conversion can be achieved, but the process is labor-intensive and results in sub-optimal quality
Solution Approach 1:
The patent replaces manual mechanical processes with automated optical systems. Specifically, it uses a light field display system with microlens arrays and spatial light modulators to automatically generate 3D content from 2D video feeds, eliminating the need for manual stereoscopic pair creation while maintaining high quality through optical principles rather than mathematical estimation
Solution Approach 2:
The patent introduces an intermediary light field display system between the 2D video source and the viewer's eyes. This intermediary uses microlens arrays to capture and redirect light rays, creating multiple views that the human visual system processes as 3D depth, thereby automating the conversion process while preserving quality
2Measurement precision
If complex mathematical models are used for depth estimation, then depth perception can be achieved, but the system complexity increases
Solution Approach 1:
The patent replaces complex mathematical depth estimation models with direct optical measurement using microlens arrays. The physical optics system naturally encodes depth information through light ray direction and focal plane positioning, eliminating the need for computational depth maps and complex algorithms while achieving accurate depth perception
Solution Approach 2:
The patent changes the fundamental parameter for depth representation from computational depth maps to physical light ray angles and focal distances. By using optical parameters (light direction, focal plane position) rather than mathematical parameters (depth values from algorithms), the system achieves accurate depth estimation with simpler mechanics
3Manufacturing precision
If resolution of displays is increased, then image quality is improved, but the immersive experience remains limited
Solution Approach 1:
The patent adds the dimension of depth perception to traditional 2D high-resolution displays by implementing a light field display system with microlens arrays. This enables the display to present multiple focal planes and viewing angles simultaneously, transforming a 2D high-resolution image into an immersive 3D light field that engages the human visual system's depth processing capabilities
Solution Approach 2:
The patent segments the display into multiple focal planes and viewing zones using microlens arrays. Each microlens creates a specific view for specific eye positions, allowing the system to deliver high resolution to multiple discrete viewing positions simultaneously, thereby enhancing immersion without sacrificing image quality
Data Source
AI summary
A three dimensional system including rendering with variable displacement.