3D Image Space Mapping to Reduce Projection and Memory Overhead
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Solution Overview
Problem
Existing methods for transforming graphical elements between different two-dimensional image spaces, such as equirectangular and fisheye, require numerous mathematical projections, leading to high processing time and memory overhead, especially when dealing with larger image spaces.
Innovation Solution
A three-dimensional image transformation method that uses a three-dimensional image space to project and map two-dimensional image spaces, reducing the number of required mathematical projections from 20 to 4, and enabling parallel processing, thereby decreasing processing time and memory footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct two-dimensional image projection is used to transform between different image spaces, then transformation accuracy is maintained, but processing time and memory overhead increase significantly
Solution Approach 1:
The patent introduces a three-dimensional image space as an intermediary between different two-dimensional image spaces. Instead of performing direct complex projections between 2D spaces (requiring 20 mathematical projections), the system transforms images through a common 3D space, reducing the number of projections to 4. This intermediary approach maintains transformation accuracy while significantly reducing processing time and computational overhead.
Solution Approach 2:
The patent transitions from operating exclusively in two-dimensional image spaces to utilizing a three-dimensional image space as a common reference frame. By elevating the transformation process to three dimensions, the system simplifies the mathematical projections required and enables more efficient parallel processing, thereby reducing processing time while maintaining accuracy.
2Reliability
If direct two-dimensional image projection is used to transform between different image spaces, then transformation completeness is achieved, but memory overhead increases
Solution Approach 1:
By using a three-dimensional image space as a mediator, the patent reduces the computational complexity of transformations between different two-dimensional image spaces. The 3D space serves as a common reference that requires fewer mathematical projections (4 instead of 20), thereby reducing the memory resources needed to store intermediate calculation results while ensuring complete and accurate transformations.
3Measurement precision
If numerous mathematical projections are used for image space transformation, then transformation accuracy is maintained, but device complexity increases
Solution Approach 1:
The three-dimensional image space acts as a simplifying intermediary that reduces the number of mathematical projections from 20 to 4. This approach maintains transformation accuracy by preserving the mathematical relationships between image spaces while significantly reducing processing complexity and making the system more suitable for implementation on specialized graphics processors.
4Power
If traditional two-dimensional projection methods are used, then processing capability is sufficient for small images, but productivity decreases for larger image spaces
Solution Approach 1:
By introducing a three-dimensional image space, the patent enables more efficient parallel processing operations. The 3D space structure allows for optimized memory access patterns and reduces the total number of mathematical projections required, thereby improving processing efficiency and productivity for both small and large image spaces while maintaining sufficient processing capability.
Data Source
AI summary
A three-dimensional image transformation, executing on one or more computer systems, can mathematically transform a first two-dimensional image space onto a second two-dimensional image space using a three-dimensional image space. The three-dimensional image transformation can project the three-dimensional image space onto the first two-dimensional image space to map the first two-dimensional image space to the three-dimensional image space. Thereafter, the three-dimensional image transformation can project the second two-dimensional image space onto the three-dimensional image space to map the three-dimensional image space to the second two-dimensional image space.