Nvidia corporation (20240177394). MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES simplified abstract
Contents
- 1 MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES
Organization Name
Inventor(s)
Pawel Kozlowski of Truckee CA (US)
Maksim Aizenshtein of Sammamish WA (US)
MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240177394 titled 'MOTION VECTOR OPTIMIZATION FOR MULTIPLE REFRACTIVE AND REFLECTIVE INTERFACES
Simplified Explanation
The patent application relates to a method for determining accurate motion vectors in situations involving translucent object surfaces, such as noisy monte carlo integration. The invention optimizes the search for real-world positions by defining the background as first path vertices visible through multiple layers of refractive interfaces. It treats the background as a single layer morphing in a chaotic way to find matching world positions, allowing for optimized algorithm execution. The techniques can apply numerical optimization methods like Newton's quadratic target to locate pixels via vector angle minimization, improving performance over linear gradient descent. The determined motion vectors can be used for services like image denoising.
- Method for determining accurate motion vectors in situations involving translucent object surfaces
- Optimization of search for real-world positions by defining the background as first path vertices visible through multiple layers of refractive interfaces
- Treatment of the background as a single layer morphing in a chaotic way to find matching world positions
- Application of numerical optimization methods like Newton's quadratic target to locate pixels via vector angle minimization
- Use of determined motion vectors for services like image denoising
Potential Applications
The technology can be applied in various fields such as computer graphics, image processing, virtual reality, and augmented reality.
Problems Solved
The technology addresses the challenge of accurately determining motion vectors in situations involving translucent object surfaces, improving performance over traditional methods like linear gradient descent.
Benefits
The benefits of this technology include enhanced accuracy in determining motion vectors, optimized algorithm execution, and improved performance in image denoising services.
Potential Commercial Applications
Potential commercial applications of this technology include software development for image processing, virtual reality applications, and computer graphics.
Possible Prior Art
One possible prior art in this field could be the use of linear gradient descent for determining motion vectors in translucent object surfaces.
Original Abstract Submitted
systems and methods relate to the determination of accurate motion vectors, for rendering situations such as a noisy monte carlo integration where image object surfaces are at least partially translucent. to optimize the search for “real world” positions, this invention defines the background as first path vertices visible through multiple layers of refractive interfaces. to find matching world positions, the background is treated as a single layer morphing in a chaotic way, permitting the optimized algorithm to be executed only once. further improving performance over the prior linear gradient descent, the present techniques can apply a cross function and numerical optimization, such as newton's quadratic target or other convergence function, to locate pixels via a vector angle minimization. determined motion vectors can then serve as input for services including image denoising.
