Nvidia corporation (20240185523). GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING simplified abstract
Contents
- 1 GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING - 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
GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING
Organization Name
Inventor(s)
Francis Williams of Brooklyn NY (US)
Karsten Kreis of Vancouver (CA)
GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185523 titled 'GENERATING COMPLETE THREE-DIMENSIONAL SCENE GEOMETRIES USING MACHINE LEARNING
Simplified Explanation
The patent application describes a technique for performing three-dimensional (3D) scene completion using machine learning models to update an initial representation of a 3D scene over time.
- The technique involves determining an initial representation of a first 3D scene.
- Executing a machine learning model to generate a first update and a second update to the initial representation at different time steps.
- The second update is generated based on a threshold applied to a set of predictions corresponding to the first update.
- Generating a 3D model of the scene based on the second update to the initial representation.
Potential Applications
This technology could be applied in various fields such as virtual reality, augmented reality, video game development, and architectural design.
Problems Solved
This technology solves the problem of efficiently completing 3D scenes by using machine learning models to update and generate accurate representations of the scenes.
Benefits
The benefits of this technology include improved accuracy and efficiency in completing 3D scenes, which can save time and resources for developers and designers.
Potential Commercial Applications
One potential commercial application of this technology could be in the development of software tools for 3D modeling and scene completion, catering to industries such as entertainment, architecture, and simulation.
Possible Prior Art
One possible prior art for this technology could be existing methods for 3D scene completion using traditional computer graphics techniques without the use of machine learning models.
Unanswered Questions
How does this technique compare to traditional methods of 3D scene completion?
This article does not provide a direct comparison between this technique and traditional methods of 3D scene completion. It would be interesting to see a study or analysis on the effectiveness and efficiency of this technique compared to traditional approaches.
What are the potential limitations or challenges of implementing this technology in real-world applications?
The article does not address any potential limitations or challenges that may arise when implementing this technology in real-world applications. It would be important to consider factors such as computational resources, data requirements, and model accuracy in practical scenarios.
Original Abstract Submitted
in various examples, a technique for performing three-dimensional (3d) scene completion includes determining an initial representation of a first 3d scene. the technique also includes executing a machine learning model to generate a first update to the initial representation at a previous time step and a second update to the initial representation at a current time step, wherein the second update is generated based at least on a threshold applied to a set of predictions corresponding to the first update. the technique also includes generating a 3d model of the 3d scene based at least on the second update to the initial representation.
