2D to 3D Image Conversion via Depth-Map Neural Network Segmentation
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Solution Overview
Problem
Existing 2D image conversion technologies fail to effectively generate 3D images using neural networks, lacking a method to convert 2D images into 3D with a three-dimensional effect by separating images into front and back views and restoring background space.
Innovation Solution
A method and apparatus that utilize a depth-map neural network to separate 2D images into front and back view images, generating a 3D image by restoring the background space between them, allowing for efficient conversion of 2D images into 3D without additional equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If 2D image conversion technology is used, then image processing can be performed, but the three-dimensional effect and visual depth perception are insufficient
Solution Approach 1:
The patent segments the 2D image into multiple layers including foreground objects, background, and depth-map layers. This segmentation enables selective processing of different image components to create three-dimensional effects while maintaining manageable processing complexity through hierarchical organization of image elements.
Solution Approach 2:
The patent transforms 2D images into 3D images by adding depth information and spatial dimensions. This dimensionality change is achieved through neural network processing that generates depth maps and reconstructs background spaces, converting planar images into stereoscopic images with perceived depth and volume.
2Manufacturing precision
If neural network processing is applied to convert 2D to 3D images, then three-dimensional effect is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing by generating depth maps and segmenting images into layers before the main 3D conversion process. This preliminary action prepares the image data in advance, allowing the neural network to focus on specific tasks like background reconstruction rather than processing the entire image from scratch, thereby reducing overall processing time.
Solution Approach 2:
The patent applies neural network processing selectively to specific regions of the image, particularly the background areas that require depth reconstruction, rather than processing the entire image uniformly. This partial action approach concentrates computational resources on areas that most benefit from 3D conversion while skipping or simplifying processing in areas where it is less critical.
3Manufacturing precision
If consecutive frames are processed individually, then each frame achieves optimal conversion quality, but processing efficiency decreases
Solution Approach 1:
The patent merges consecutive frames that contain similar content by identifying and grouping them together. Instead of processing each frame independently, the system combines multiple similar frames and processes them as a unit, significantly improving processing efficiency while maintaining conversion quality through the use of temporal information and frame interpolation techniques.
Data Source
AI summary
A method and apparatus for image conversion according to an embodiment of the present disclosure includes receiving original image data, separating the original image data into a front view image and a back view image for performing 3D conversion processing of the original image data, and generating a converted 3D image by restoring a background space between the front view image and the back view image using a 3D conversion processing neural network. The 3D conversion processing neural network according to the present disclosure may be a deep neural network generated by machine learning, and input and output of images may be performed in an Internet of things environment using a 5G network.