Nvidia corporation (20240161396). UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION simplified abstract
Contents
- 1 UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION - 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
UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION
Organization Name
Inventor(s)
Jeevan Devaranjan of Toronto (CA)
UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240161396 titled 'UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION
Simplified Explanation
In a patent application abstract, a rule set or scene grammar is used to generate a scene graph representing the structure and visual parameters of objects in a scene. A renderer then takes this scene graph as input, along with a library of content for assets identified in the scene graph, to generate a synthetic image of the scene without manual placement of objects. This technology can be used to create training data for navigational applications, virtual worlds for games, or virtual reality experiences.
- Rule set or scene grammar used to generate a scene graph
- Renderer takes scene graph as input to generate synthetic image
- No manual placement of objects required
- Can be used for training data, virtual worlds, and virtual reality experiences
Potential Applications
This technology can be applied in various fields such as:
- Training data generation for real-world navigational applications
- Creation of virtual worlds for games
- Development of virtual reality experiences
Problems Solved
This technology addresses the following issues:
- Manual placement of objects in a scene
- Time-consuming process of scene creation
- Lack of automated tools for generating synthetic images
Benefits
The benefits of this technology include:
- Efficient generation of synthetic images
- Consistent scene structure without manual intervention
- Versatile applications in different industries
Potential Commercial Applications
This technology has potential commercial applications in:
- Gaming industry for creating virtual worlds
- Training and simulation industry for generating realistic environments
- Entertainment industry for developing immersive virtual reality experiences
Possible Prior Art
One possible prior art for this technology is the use of procedural generation techniques in computer graphics to create scenes and environments automatically.
Unanswered Questions
How does this technology compare to traditional manual scene creation methods?
This technology eliminates the need for manual placement of objects in a scene, making the process more efficient and automated.
What are the limitations of using a rule set or scene grammar to generate a scene graph?
The limitations may include the complexity of defining rules for different types of scenes and the potential for inaccuracies in the generated scene graph.
Original Abstract Submitted
a rule set or scene grammar can be used to generate a scene graph that represents the structure and visual parameters of objects in a scene. a renderer can take this scene graph as input and, with a library of content for assets identified in the scene graph, can generate a synthetic image of a scene that has the desired scene structure without the need for manual placement of any of the objects in the scene. images or environments synthesized in this way can be used to, for example, generate training data for real world navigational applications, as well as to generate virtual worlds for games or virtual reality experiences.
