Nvidia corporation (20240212261). REAL-TIME RENDERING WITH IMPLICIT SHAPES simplified abstract
Contents
REAL-TIME RENDERING WITH IMPLICIT SHAPES
Organization Name
Inventor(s)
Towaki Alan Takikawa of Toronto (CA)
Karsten Julian Kreis of Vancover (CA)
Charles Loop of Mercer Island WA (US)
Morgan Mcguire of Waterloo (CA)
REAL-TIME RENDERING WITH IMPLICIT SHAPES - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240212261 titled 'REAL-TIME RENDERING WITH IMPLICIT SHAPES
Simplified Explanation: The patent application describes systems and methods for rendering complex surfaces or geometry using neural signed distance functions (SDFs) to efficiently capture multiple levels of detail and reconstruct multi-dimensional geometry with high image quality.
Key Features and Innovation:
- Use of neural signed distance functions (SDFs) to capture multiple levels of detail efficiently.
- Reconstructing multi-dimensional geometry with high image quality.
- Representing complex shapes in a compressed format with high visual fidelity.
- Generalizing across different geometries from a single learned example.
- Utilizing extremely small multi-layer perceptrons (MHLPS) with an octree-based feature representation for the learned neural SDFs.
Potential Applications: The technology can be applied in various industries such as gaming, virtual reality, computer-aided design, medical imaging, and more.
Problems Solved: The technology addresses the challenges of efficiently rendering complex surfaces or geometry with high image quality and capturing multiple levels of detail.
Benefits:
- Improved efficiency in rendering complex surfaces.
- High image quality in reconstructing multi-dimensional geometry.
- Compression of complex shapes with high visual fidelity.
- Generalization across different geometries from a single learned example.
Commercial Applications: The technology can be used in industries such as gaming, virtual reality, computer-aided design software, medical imaging technology, and more, enhancing the visual quality and efficiency of rendering complex surfaces.
Questions about the Technology: 1. How does the use of neural signed distance functions improve the rendering of complex surfaces? 2. What are the potential limitations of using extremely small multi-layer perceptrons in this technology?
Frequently Updated Research: Stay updated on advancements in neural signed distance functions and multi-layer perceptrons in rendering complex surfaces and geometry for various applications.
Original Abstract Submitted
systems and methods are described for rendering complex surfaces or geometry. in at least one embodiment, neural signed distance functions (sdfs) can be used that efficiently capture multiple levels of detail (lods), and that can be used to reconstruct multi-dimensional geometry or surfaces with high image quality. an example architecture can represent complex shapes in a compressed format with high visual fidelity, and can generalize across different geometries from a single learned example. extremely small multi-layer perceptrons (mhlps) can be used with an octree-based feature representation for the learned neural sdfs.
