2D to 3D Image Conversion Using Segmented Depth Map Heuristics
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Solution Overview
Problem
Current methods for converting 2D videos to 3D are either labor-intensive and expensive, requiring manual tracing of objects and depth painting, or produce poor quality 3D images when automated, failing to balance quality and real-time processing needs.
Innovation Solution
A method and system that uses pre-defined heuristic rules to automatically generate depth maps from 2D images, allowing for real-time conversion and optional user-defined rule adjustments to enhance 3D image quality, utilizing CPU, GPU, FPGA, or ASIC processing to produce 3D images in various formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual roto-scoping is used to convert 2D videos to 3D, then 3D image quality is improved, but conversion time and cost increase significantly
Solution Approach 1:
The patent segments the depth map generation process into multiple passes: a first pass using simplified heuristics for real-time conversion, and a second pass using complex mathematical analysis for enhanced quality. This segmentation allows the system to deliver acceptable 3D quality at real-time speeds while offering optional quality enhancement for non-real-time applications.
Solution Approach 2:
The patent implements dynamic quality adjustment by allowing the system to switch between different depth map generation methods based on real-time requirements. The first pass uses fast heuristic methods for immediate results, while the second pass dynamically enhances quality using more computationally intensive methods when time permits, creating a flexible adaptive system.
2Productivity
If fully automated heuristic methods are used for 2D to 3D conversion, then conversion speed is improved, but 3D image quality deteriorates
Solution Approach 1:
The patent divides the conversion process into two distinct passes: a first real-time pass using fast heuristic depth map generation, and a second enhanced pass using sophisticated mathematical analysis. This segmentation enables the system to provide real-time conversion with acceptable quality, while offering an optional enhancement phase for superior quality when time allows.
Solution Approach 2:
The first pass performs preliminary depth map generation using fast heuristics to establish a baseline 3D conversion in real-time. This preliminary action creates an initial 3D image that can be displayed immediately, while the second pass subsequently refines the depth map using more accurate but slower methods to enhance quality for frames where time permits.
3Manufacturing precision
If complex mathematical analysis is used to generate depth maps, then 3D image quality is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments computational complexity into two levels: the first pass uses simple heuristic rules with low computational complexity for real-time processing, while the second pass employs complex mathematical analysis for enhanced quality. This segmentation allows the system to manage computational resources efficiently by applying appropriate complexity only when time and resources permit.
Solution Approach 2:
The patent applies partial mathematical analysis by using sophisticated methods only for the second enhanced pass rather than applying them to every frame. This partial action approach allows the system to achieve improved quality for select frames without the computational burden of applying complex analysis universally, balancing quality enhancement with processing efficiency.
Data Source
AI summary
A method for converting a 2D images and videos to 3D includes applying a set of pre-defined heuristic rules to assign a depth value for each pixel of a two-dimensional (2D) image source based on pixel attributes to generate an initial default depth map, refining the pre-defined heuristic rules to produce customized heuristic rules, applying the customized heuristic rules to the initial default depth map to produce a refined depth map, and rendering a three-dimensional (3D) image in a predefined format using the refined depth map.