18505283. UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION simplified abstract (NVIDIA Corporation)
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 18505283 titled 'UNSUPERVISED LEARNING OF SCENE STRUCTURE FOR SYNTHETIC DATA GENERATION
Simplified Explanation
The abstract describes a method for generating a scene graph from a rule set or scene grammar, which can then be used by a renderer to create a synthetic image of a scene without manual placement of objects.
- Scene graph generation from rule set/scene grammar
- Renderer uses scene graph to generate synthetic image
- No manual placement of objects required
Potential Applications
The technology can be used for:
- Generating training data for navigational applications
- Creating virtual worlds for games or virtual reality experiences
Problems Solved
The technology solves the following problems:
- Manual placement of objects in a scene
- Tedious scene creation process
Benefits
The technology offers the following benefits:
- Efficient scene generation
- Consistent scene structure
- Versatile applications in various industries
Potential Commercial Applications
The technology can be applied in the following commercial areas:
- Game development
- Virtual reality experiences
- Training simulations
Possible Prior Art
One possible prior art for this technology could be the use of procedural generation techniques in computer graphics to create scenes automatically.
Unanswered Questions
How does the technology handle complex scenes with multiple objects and interactions?
The article does not provide details on how the technology manages complex scenes with various objects and interactions. Further information on the scalability and performance of the system would be helpful.
What are the limitations of the technology in terms of scene complexity and realism?
The article does not address the potential limitations of the technology in handling highly complex scenes or achieving photorealistic results. Understanding the boundaries of the system would be crucial for assessing its practical applications.
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.
