Environment Map Fragment Upscaling for Viewpoint-Based Rendering
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Solution Overview
Problem
Conventional environment map techniques for panoramic images consume vast amounts of data and computational resources, limiting image quality and suitability for digital image generation.
Innovation Solution
Implement environment map upscaling techniques using machine learning models like super-resolution convolutional neural networks and interpolation methods to enhance the resolution of environment map fragments, applying lighting conditions to digital objects for improved visual quality with reduced resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional environment map techniques are used for panoramic images, then complete panoramic coverage is achieved, but data consumption and computational resources increase vastly
Solution Approach 1:
The environment map is divided into multiple map fragments corresponding to different viewpoints. Instead of processing or storing complete high-resolution panoramic images, the system segments the environment map into smaller manageable fragments that can be selectively loaded and processed based on the current viewpoint, significantly reducing data consumption while maintaining complete panoramic coverage capability
Solution Approach 2:
The system applies upscaling processing selectively to only the map fragment corresponding to the current viewpoint rather than processing the entire environment map. This local quality enhancement approach improves visual quality where needed (at the current viewpoint) while avoiding the computational overhead of processing all panoramic data, thus reducing overall computational resource consumption
2Manufacturing precision
If high resolution environment maps are used for digital image generation, then image quality improves, but computational resource consumption increases
Solution Approach 1:
The system extracts only the necessary map fragment corresponding to the current viewpoint from the complete environment map for upscaling and processing. By taking out only the relevant portion needed for the current rendering task, the system achieves high image quality for the visible area while avoiding the computational expense of processing the entire high-resolution environment map
Solution Approach 2:
The system transitions from processing complete 360-degree panoramic environment maps to processing individual 2D map fragments at the current viewpoint. This dimensional reduction allows high-resolution upscaling to be applied efficiently to smaller regions, improving image quality while reducing computational resource consumption by working in a reduced dimensional space
3Adaptability or versatility
If complete environment maps are processed for all viewpoints, then all panoramic views are available, but processing time and data storage increase
Solution Approach 1:
The system pre-divides the environment map into multiple map fragments corresponding to different viewpoints during map creation. This preliminary segmentation allows the system to quickly load and process only the relevant fragment for the current viewpoint without needing to process or store complete high-resolution data for all possible viewpoints, reducing processing time while maintaining full viewpoint coverage capability
Solution Approach 2:
Instead of processing complete environment maps for all viewpoints, the system performs partial processing on only the map fragment corresponding to the current viewpoint. This partial action approach provides sufficient visual quality for the current view without the excessive processing time and data storage requirements of complete panoramic processing
Data Source
AI summary
Environment map upscaling techniques are described for digital image generation. A digital object and an environment map are received, the environment map defines lighting conditions within a panoramic view of an environment. A viewpoint is detected with respect to the panoramic view in the environment map. A map fragment is identified from the environment map based on the detected viewpoint and an upscaled map fragment is formed by upscaling the map fragment. A digital image is then generated based on the upscaled map fragment and the digital object as having the lighting conditions applied based on the environment map.