2D to 3D Image Conversion Using Segmented Layer Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The process of converting 2D motion pictures to 3D is labor-intensive and time-consuming, particularly due to the need for manual object separation and filling occlusion regions, which can result in visible artifacts and is not scalable for meeting tight cinematic release schedules.
Innovation Solution
A systematic process architectural model for converting 2D image sequences to 3D, involving the collection of image data cues and other processing information, using a multi-mode computing structure for object processing and occlusion region filling, and an automated system for rendering and updating render data records to ensure efficient and adaptable 3D image production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual object separation and occlusion region filling are used to convert 2D to 3D, then visual quality can be maintained, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent segments the 2D image into multiple layers including foreground objects, background, and occlusion regions. This segmentation allows automated processing of each layer with specific algorithms, maintaining visual quality while reducing manual intervention. The layered structure enables parallel processing and automated occlusion filling.
Solution Approach 2:
The patent performs preliminary depth map generation and object separation before final 3D rendering. By pre-processing the 2D image to identify objects, estimate depths, and separate layers in advance, the system reduces the time required for the actual conversion process while maintaining quality through automated algorithms.
2Manufacturing precision
If manual processing methods are used for 2D to 3D conversion, then quality can be maintained, but scalability for tight release schedules is limited
Solution Approach 1:
The patent replaces manual mechanical processing with automated computational algorithms. Computer vision algorithms automatically perform object detection, depth estimation, and layer separation, while machine learning models handle occlusion region filling. This substitution enables scalable processing that can meet tight release schedules without sacrificing quality.
Solution Approach 2:
The patent changes the processing parameters from manual operation to automated algorithmic operation. By adjusting parameters such as depth map resolution, layer separation thresholds, and occlusion filling confidence levels, the system maintains quality while achieving high productivity through automated batch processing.
3Productivity
If automated algorithms are used for object processing, then productivity increases, but handling frequent changes and updates becomes more complex
Solution Approach 1:
The patent implements a dynamic processing pipeline where algorithms can be selectively applied to different image regions and objects. The system dynamically adjusts processing parameters and algorithm selection based on image content analysis, enabling efficient handling of frequent changes without requiring complete re-processing of entire scenes.
Solution Approach 2:
The patent performs preliminary analysis to identify objects, regions, and features that require processing before applying automated algorithms. This pre-processing step creates a structured representation that simplifies subsequent automated processing and makes the system more adaptable to changes, as only affected regions need re-processing.
4Manufacturing precision
If comprehensive processing information is collected for accurate 3D conversion, then visual quality improves, but the amount of data and processing complexity increases
Solution Approach 1:
The patent applies different processing depths and information collection levels to different regions of the image based on their importance. High-priority regions such as foreground objects receive comprehensive processing with detailed depth maps and multiple views, while background regions use simplified algorithms. This local quality approach maintains overall visual quality while reducing total data volume.
Solution Approach 2:
The patent collects processing information selectively rather than comprehensively for all image elements. It focuses on gathering essential depth and motion data for objects that significantly impact 3D perception, using partial information for less critical regions. This approach achieves acceptable conversion accuracy with reduced data requirements.
Data Source
AI summary
The present invention discloses methods of digitally converting 2D motion pictures or any other 2D image sequences to stereoscopic 3D image data for 3D exhibition. In one embodiment, various types of Image data cues can be collected from 2D source images by various methods and then used for producing two distinct stereoscopic 3D views. Embodiments of the disclosed methods can be implemented within a highly efficient system comprising both software and computing hardware. The architectural model of some embodiments of the system is equally applicable to a wide range of conversion, re-mastering and visual enhancement applications for motion pictures and other image sequences, including converting a 2D motion picture or a 2D image sequence to 3D, re-mastering a motion picture or a video sequence to a different frame rate, enhancing the quality of a motion picture or other image sequences, or other conversions which facilitate further improvement in visual image quality within a projector to produce the enhanced images.