Ray Tracing Data Transformation With Segmented Inverse Mapping
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Solution Overview
Problem
Existing ray tracing systems face inefficiencies in transforming data between different coordinate spaces, leading to errors and increased computational load due to the use of inverse transformation matrices, particularly affecting precision for rays near the origin of geometric models.
Innovation Solution
A method involving a two-step transformation process is employed, where the inverse transformation is decomposed into inverse translation followed by inverse linear mapping, avoiding the calculation of a combined inverse matrix, thereby maintaining accuracy and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inverse transformation matrices are used to transform ray data from world space to model space, then the transformation can be performed in a single step, but precision is degraded due to rounding errors especially for rays near the origin
Solution Approach 1:
The patent segments the inverse transformation process into two distinct steps: first applying the inverse translation matrix, then applying the inverse rotation matrix. This segmentation prevents the accumulation of rounding errors that occurs when using a single combined inverse transformation matrix, thereby improving transformation precision for rays near the origin while maintaining reasonable process complexity.
2Productivity
If a single inverse transformation matrix is calculated and used, then computational overhead is reduced, but precision errors increase due to rounding
Solution Approach 1:
The patent divides the transformation process into two separate matrix operations (inverse translation followed by inverse rotation) rather than using a single combined matrix. This segmentation improves precision by minimizing rounding errors while the matrices are pre-calculated and stored, maintaining computational efficiency during ray tracing operations.
3Speed
If the transformation process is simplified by using combined matrices, then processing speed increases, but accuracy decreases for critical ray intersections
Solution Approach 1:
The patent applies segmentation by separating the inverse transformation into distinct translation and rotation matrix operations. This allows the system to maintain processing speed through pre-calculated matrices while achieving higher intersection testing accuracy by minimizing rounding errors in the transformation process.
Solution Approach 2:
The patent performs preliminary calculation and storage of the inverse translation matrix and inverse rotation matrix before ray tracing begins. This preliminary action allows the transformation to proceed efficiently during rendering while ensuring accuracy through the segmented application of pre-computed matrices rather than calculating a single combined inverse matrix.
Data Source
AI summary
A computing system and method for processing data in which a forward transformation indication is received defining a transformation from a first space to a second space. A transformation is performed on input data from the second space to the first space to determine transformed data by performing a reverse translation operation on the input data, wherein the reverse translation operation is the reverse of a translation defined by the forward transformation indication. An inverse linear mapping operation is performed on the result of the reverse translation operation, wherein the inverse linear mapping operation is the inverse of a linear mapping defined by the forward transformation indication. The transformed data is processed in the computing system to render an image of a scene.