Tile-Based Texture Synthesis for Aerial Imagery Repetition Reduction
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Solution Overview
Problem
Existing methods for real-time tile-based texture synthesis of aerial imagery suffer from noticeable repetition, especially in flat regions, and require large data storage, making them unsuitable for offline applications without stable internet connections.
Innovation Solution
The introduction of a Puzzle Biome approach, which uses segmentation masks and color variations to create realistic and varied textures for flat regions, reducing repetition and data requirements by randomly selecting segmentation masks and color variations for each tile based on geographic location and elevation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If prior art approaches are used for texture synthesis, then real-time rendering is achieved, but visible repetition of features occurs in flat regions producing low-quality results
Solution Approach 1:
The patent segments the terrain into different biomes and further divides each biome into multiple patches with unique texture attributes. Each patch is assigned distinct characteristics such as texture offset, color variation, and scale factors, ensuring that adjacent patches display varied features rather than repetitive patterns. This segmentation approach directly addresses the repetition problem by breaking up uniform regions into diverse segments.
Solution Approach 2:
The patent implements local quality by assigning unique texture parameters to each patch within a biome. Each patch has locally optimized attributes including random color variations, different texture scales, and unique offset values. This ensures that each local region has distinct visual characteristics while maintaining overall biome consistency, thereby eliminating visible repetition in flat regions.
2Reliability
If large collections of biome texture variations are used to reduce repetition, then quality improves, but data storage requirements become prohibitively huge
Solution Approach 1:
The patent employs dynamic texture generation where base textures are procedurally modified at runtime using random variation parameters. Instead of storing multiple static texture variations, the system dynamically adjusts texture properties such as color, scale, and offset based on patch-specific parameters. This dynamic approach generates diverse visual outcomes from a single base texture, dramatically reducing storage requirements while maintaining quality.
Solution Approach 2:
The patent changes texture parameters dynamically rather than storing multiple texture variants. Each patch applies parameter transformations including color scaling, texture offset adjustments, and scale modifications to a base texture. These parameter changes create the appearance of multiple unique textures while actually using a single stored texture file, thus reducing data storage requirements from huge collections to minimal base sets.
3Adaptability or versatility
If offline synthesis is performed without stable internet connection, then real-time streaming is not required, but repetition becomes more noticeable and quality decreases
Solution Approach 1:
The patent implements self-service by enabling the system to generate diverse textures autonomously without requiring external texture assets or internet connections. The procedural generation system uses locally stored base textures and algorithmically creates variations through parameter manipulation and noise functions. This self-sufficient approach allows offline operation with high quality output, eliminating the repetition problem that plagues traditional offline synthesis methods.
Data Source
AI summary
Input coordinates identify a tile on the globe and a biome is identified for each pixel of the tile. We choose a random segmentation mask of the corresponding biome for the tile. Every segment of the mask carries a unique identifier and access to a random value via a corner reference. These parameters ensure that (when executed in parallel) each pixel with the same identifier and random value is handled the same way. For each pixel of the tile, using the selected mask, we identify the segment corresponding to that pixel, we retrieve a color variation for that pixel based upon the segment identifier and the random value. We thus choose a color variation for each segment of a segmentation mask. Pixels in the same segment are treated the same way. Instead of a color variation for flat areas (crop fields) we choose a density variation for regions like forests.