Adaptive Luminosity Function for 3D Rendering
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Solution Overview
Problem
Current computer-implemented methods for rendering three-dimensional scenes are inefficient due to fixed sampling densities, lack of adaptability, and inability to guarantee sampling reliability, leading to inaccurate images and high computational costs, especially as image resolution increases.
Innovation Solution
A computer-implemented method that constructs a luminosity function using a set of samplings and stored input data to provide a continuous representation of light impinging on the image plane, allowing for adaptive sampling and efficient reconstruction, with coefficients computed through a linear system that accounts for the features of the scene and lighting conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-adaptive sampling processes with fixed sampling density are used, then the sampling process is simple and fast, but the rendering accuracy deteriorates and substantial computation time is required to obtain images with few artifacts
Solution Approach 1:
The patent implements adaptive sampling where the sampling density is dynamically adjusted based on the image regions. Different regions of the image plane are assigned different sampling densities according to their complexity and visual importance, allowing the system to optimize between rendering speed and accuracy automatically during the rendering process
2Manufacturing precision
If adaptive sampling processes are used to determine regions requiring high or low sampling density, then rendering accuracy improves, but the ability to quantitatively determine minimum number of samples is lost and reliability cannot be guaranteed
Solution Approach 1:
The patent employs feedback mechanisms where the sampling process continuously monitors the quality of reconstructed image regions and adjusts sampling density accordingly. This feedback loop ensures that sufficient samples are collected to guarantee reliability while avoiding unnecessary sampling in low-complexity regions
Solution Approach 2:
The system dynamically changes sampling parameters (density, distribution) based on real-time analysis of image characteristics. By adjusting these parameters adaptively, the system maintains both high accuracy and reliable quantitative control over the sampling process
3Manufacturing precision
If reconstruction processes assign luminosity and colour value to each pixel for a given rasterisation, then the representation is obtained for specific resolution, but the representation becomes unsuitable for different rasterisations and computation must be repeated
Solution Approach 1:
The patent creates a universal representation system that can serve multiple rasterisation resolutions simultaneously. By using a hierarchical or multi-scale approach, the same sampling and reconstruction framework can generate images at different resolutions without requiring separate processing pipelines, thus achieving both accuracy and versatility
4Manufacturing precision
If the number of samples is proportional to the number of pixels for reconstruction, then rendering accuracy is maintained, but the computational effort scales steeply and image resolution is strongly limited
Solution Approach 1:
The patent applies local quality principles by assigning different sampling densities to different regions of the image. Rather than uniformly sampling all pixels, the system concentrates samples in regions with high visual complexity and reduces sampling in smooth or low-detail regions, significantly reducing overall computational complexity while maintaining accuracy where needed
Data Source
AI summary
Computer implemented method for rendering an image of a three-dimensional scene on an image plane by encoding at least a luminosity in the image plane by a luminosity function. The value of the luminosity can be computed at substantially each point of the image plane by using a set of stored input data describing the scene. The method includes constructing the luminosity function as equivalent to a first linear combination involving the functions of a first set of functions, and computing at least the value of the coefficients of the first linear combination, by solving a first linear system, obtained by using at least the functions of the first linear combination, at least a subset of the first subset of the image plane, and the luminosity at the points of said subset. The method further includes storing the value of the coefficients of the first linear combination and at least the information needed to associate each coefficient to the function multiplying said coefficient in the first linear combination. The first set of functions comprises each function of a second set of functions satisfying a selection condition, which depends at least on the set of stored input data. Moreover, the points of the first subset are distributed according to a first distribution criterion, which depends on the location of the support of at least a function of the first set of functions.