Shadow Horizon Propagation for Elevation Data Grids
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Solution Overview
Problem
Existing methods for generating shadow maps in complex topography are computationally expensive and limited by the accuracy of discrete azimuth directions, particularly when dealing with large 2.5D elevation data sets, which are often processed in near real-time.
Innovation Solution
A computer-implemented method that receives elevation data, determines the direction to a light source, selects a shadow frontline propagation mode based on the light source's quadrant, and recursively updates a shadow horizon to generate a binary shadow mask, allowing for efficient and fast shadow map generation by propagating the shadow horizon along the elevation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional viewshed computation methods are used for each point in complex topography, then shadow mapping accuracy is improved, but computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the elevation data into discrete grid cells and processes shadow propagation in a systematic sequence across these segments. By segmenting the computational domain into manageable grid cells that can be processed in order, the method achieves both accuracy in complex topography and improved processing efficiency through localized computations rather than global viewshed analysis for each point
Solution Approach 2:
The patent performs preliminary classification of grid cells into illuminated and shadowed categories based on horizon height comparisons before final shadow mask generation. This preliminary action of determining sky view status for each grid point in advance allows for optimized processing and reduces the computational burden during the actual shadow mapping operation
2Productivity
If discrete azimuth directions are used for viewshed computation, then processing speed is improved, but shadow mapping accuracy deteriorates
Solution Approach 1:
The patent transitions from discrete azimuthal sampling to a continuous horizon height dimension by comparing elevation values across grid cell boundaries. Instead of sampling at discrete angles, the method uses the vertical dimension (elevation differences) to determine sky view status, effectively changing the problem from angular sampling to elevation-based geometric analysis, which provides both accuracy and efficiency
Solution Approach 2:
The patent changes the fundamental parameter from azimuthal direction (angular measurement) to horizon height (elevation measurement). By using the height of the horizon relative to each grid point as the determining factor for sky view status, the method achieves accurate shadow mapping without relying on discrete azimuth sampling, thus improving both precision and processing speed
3Loss of time
If large 2.5D elevation data sets are processed in near real-time, then operational responsiveness is improved, but computational resource requirements increase
Solution Approach 1:
The patent segments the large elevation dataset into individual grid cells that can be processed independently and in parallel. This segmentation allows computational resources to be distributed across multiple smaller tasks, enabling near real-time processing of large datasets without requiring excessive集中 computational power at any single point
Solution Approach 2:
The patent replaces traditional mechanical viewshed computation methods with an optimized algorithmic approach that uses systematic horizon height comparisons. This substitution of computational mechanics reduces the processing power required while maintaining real-time capability, as the new method requires fewer computational operations per grid point compared to traditional viewshed algorithms
Data Source
Figure 1A~1C
Figure 2
Figure 3~4
AI summary
A computer-implemented method for creating a shadow mask representing irradiation of a physical scene based upon a plurality of elevation grid points of an array of elevation data associated with the physical scene. Elevation point data are received and stored in array format, and a shadow frontline is propagated across the array based on a quadrant associated with a light source. A shadow horizon is projection onto successive lines of elevation data generating a binary shadow mask based on whether a specified elevation point lies above or below the shadow horizon. The direction of the light source may be transformed to reflect curvature of an underlying surface of the physical scene.