2-D to 3-D Conversion Using Segmentation Layers for Dynamic Objects
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Solution Overview
Problem
Current 2-D to 3-D image conversion methods face challenges such as excessive computational burden, inadequate scene refinement, and inability to handle dynamic objects, large image sets, and varying levels of detail, leading to visual distortions and inefficiencies in rendering.
Innovation Solution
The method involves acquiring and processing a sequence of 2-D images to generate camera and static geometry, using segmentation layers with weighted values for static and dynamic features, and iteratively calculating scene geometry to create 3-D renderings, allowing for interactive refinement and accurate representation of scene models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional 2-D to 3-D conversion methods are used, then 3-D images can be generated, but visual distortions and anomalies occur due to inadequate scene refinement
Solution Approach 1:
The patent segments the 3-D conversion process into distinct stages: 2-D image acquisition, camera geometry generation, static geometry extraction, segmentation layer creation with static and dynamic features, and iterative 3-D rendering. This segmentation allows each stage to be optimized independently, improving accuracy without proportionally increasing overall complexity
Solution Approach 2:
The patent performs preliminary actions by generating camera geometry and static geometry from 2-D images before the actual 3-D rendering process. Segmentation layers with weighted static and dynamic features are created in advance, allowing the iterative refinement process to start from a pre-processed state, reducing visual distortions early in the pipeline
2Manufacturing precision
If complete scene reconstruction is performed to achieve accurate 3-D representation, then visual quality improves, but computational burden increases excessively
Solution Approach 1:
The patent applies local quality by creating segmentation layers that differentiate between static and dynamic features with weighted values. Instead of uniformly processing all scene elements, the system focuses computational resources on dynamic features that require higher precision, while static features use standardized processing, reducing overall computational burden while maintaining accuracy where needed
Solution Approach 2:
The patent uses iterative refinement where the 3-D scene is reconstructed through multiple passes, each refining the previous result. The process stops when convergence is achieved or a predetermined number of iterations is reached, avoiding excessive computation while ensuring sufficient accuracy through progressive refinement rather than complete exhaustive processing
3Quantity of substance
If traditional conversion methods process large image sets, then comprehensive scene coverage is achieved, but rendering efficiency decreases
Solution Approach 1:
The patent segments large image sets into smaller batches processed through the iterative refinement pipeline. Each batch generates camera geometry and static geometry independently, then combines results in the 3-D scene. This segmentation allows parallel processing of multiple image batches, maintaining comprehensive scene coverage while improving rendering efficiency through distributed computation
Solution Approach 2:
The patent performs preliminary processing on large image sets by extracting camera geometry and static geometry before the main 3-D rendering process. This pre-processing step organizes and structures the data from large image sets into reusable geometric models, allowing the subsequent rendering stages to operate more efficiently on pre-organized data rather than raw images
4Ease of manufacture
If static geometry methods are used for 3-D conversion, then processing is simpler, but dynamic objects cannot be handled accurately
Solution Approach 1:
The patent introduces dynamics by creating segmentation layers that explicitly differentiate between static and dynamic features with weighted values. The iterative refinement process updates both static and dynamic geometry, allowing dynamic objects to be tracked and rendered accurately across multiple frames while maintaining the simplicity of the overall processing pipeline through structured layer management
Solution Approach 2:
The patent applies local quality by assigning different processing characteristics to static and dynamic features within the segmentation layers. Static features use standardized processing for simplicity, while dynamic features receive enhanced processing with motion tracking and temporal consistency checks, allowing the system to handle dynamic objects accurately without complicating the entire processing pipeline
Data Source
AI summary
The present invention is directed to systems and methods for controlling 2-D to 3-D image conversion. The system and method includes receiving an image and masking the objects in the image using segmentation layers Each segmentation layer can have weighted values for static and dynamic features. Iterations are used to form the final image which, if desired, can be formed using less than all of the segmentation layers. A final iteration can be run with the weighted values equal for static and dynamic features.