US20250095229
2025-03-20
Physics
G06T11/001
The patent application outlines a method for generating images of environments using neural radiance fields. This involves utilizing one or more neural networks to detect static and dynamic features within a given environment. These identified features are then used to create a representation of the environment. The approach aims to enhance the efficiency of image generation in environments with varying elements.
The application focuses on processing resources required for generating environmental representations. It specifically addresses the use of neural networks to produce images that depict both static and dynamic objects. This technology is particularly relevant for applications involving autonomous machines, where optimizing memory, time, and computational resources is crucial.
Autonomous machines often face challenges in maneuvering through environments due to the substantial resources needed to assess their surroundings. The proposed method seeks to reduce the memory, time, and computing power required to represent dynamic environments. By improving these aspects, the technology can facilitate more efficient operations of autonomous systems.
The patent includes numerous illustrations demonstrating various components and processes involved in the scene generation system. These include examples of scene rendering, decomposition, image generation, and hybrid scene creation. Additionally, diagrams illustrate static and dynamic scene composition and decomposition methods, as well as the architecture of systems like autonomous vehicles.
The detailed description provides insight into the components and functions of the system. It covers various modules, controllers, engines, and components involved in the process, which may be implemented through hardware, firmware, or software. The description emphasizes flexibility in implementation, allowing for discrete or distributed components across different configurations.