Neural Network Image Rendering for 360-Degree Foreground Background Separation
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Solution Overview
Problem
Current 360-degree camera systems face challenges in efficiently generating high-quality omnidirectional images by accurately distinguishing between foreground and background elements, which affects the synthesis of 3D space views.
Innovation Solution
The method involves determining sample points on camera rays using a virtual cylindrical coordinate system, applying view-generation information to neural networks trained for foreground and background image generation, and blending their results to create a rendered image, with the neural networks being trained based on pixel values and loss functions to minimize differences between rendered and input images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single neural network is used for rendering both foreground and background, then the device complexity is reduced, but the manufacturing precision of image quality deteriorates
Solution Approach 1:
The patent divides the rendering task into two separate neural networks: a first neural network for foreground rendering and a second neural network for background rendering. This segmentation allows each network to be optimized for its specific task, improving overall image quality while maintaining manageable device complexity through functional division.
2Manufacturing precision
If high-resolution rendering is applied to the entire 360-degree image, then the manufacturing precision of image quality is improved, but the loss of substance (memory usage) increases
Solution Approach 1:
The patent applies different rendering resolutions to different regions of the 360-degree image based on their importance. High-resolution rendering is applied to the foreground region where detailed quality is critical, while lower-resolution rendering is applied to the background region. This local quality differentiation maintains overall image quality while significantly reducing memory usage requirements.
3Ease of operation
If the entire 360-degree image is processed uniformly, then the ease of operation is maintained, but the productivity of image processing efficiency deteriorates
Solution Approach 1:
The patent segments the 360-degree image processing into distinct foreground and background processing pipelines. By separating the processing tasks and applying specialized techniques to each segment (high-resolution for foreground, lower-resolution for background), the system achieves improved processing efficiency while maintaining operational simplicity through automated region classification.
Data Source
AI summary
A method and apparatus with image processing are provided. A method includes determining sample points sampled on a camera ray, wherein the camera ray is based on view-generation information comprising a scene viewing-position and a scene-viewing direction, determining location statuses of the respective sample points based on a virtual cylindrical coordinate system defined by a center and a radius, projecting and rendering a value of a pixel corresponding to the camera ray by, according the location statuses, applying the view-generation information to a first neural network to generate a first rendering result and to a second neural network to generate a second rendering result, wherein the first neural network has been trained to generate foreground images and the second neural network has been trained to generate background images, and generating a rendered image based on blending the first rendering result and the second rendering result.