Neural Scene Graph Object Placement for Faster 3D Rendering
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Solution Overview
Problem
Existing methods for rendering graphical scenes lack efficiency in generating realistic environments, particularly in optimizing the efficiency of the scene generation, and the use of neural networks to identify locations for objects within these scenes, which can be memory and resource-intensive.
Innovation Solution
Utilizing a scene generator that employs neural networks to generate scene graphs, analyze spatial relationships, and execute object placement, reducing resource utilization through generative AI techniques and efficient object generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional rendering techniques are used to generate graphical scenes, then scene generation can be performed, but significant memory, time, and computing resources are consumed
Solution Approach 1:
The patent segments the scene generation process into distinct components: neural network-based location identification, object placement determination, and graphical scene generation. By dividing the rendering process into these modular stages, the system can optimize resource allocation at each step, reducing overall computing resource consumption while maintaining generation speed.
Solution Approach 2:
The patent performs preliminary actions by using neural networks to pre-identify object locations and determine placement decisions before actual scene rendering. This preliminary analysis phase separates computationally intensive identification tasks from the rendering phase, allowing for more efficient resource utilization during the actual scene generation process.
2Measurement precision
If neural networks are used to identify object locations in graphical scenes, then accurate placement can be achieved, but memory and computing resources increase significantly
Solution Approach 1:
The patent introduces an intermediary scene graph structure that mediates between the neural network's location identification and the final graphical scene. The scene graph serves as a lightweight intermediate representation that stores only essential spatial relationships and object placements, significantly reducing memory requirements compared to storing complete scene data while maintaining location identification accuracy.
Solution Approach 2:
The patent extracts only the essential spatial information and object placement data from the neural network's comprehensive analysis. By taking out and storing only the critical location identifiers and placement decisions in the scene graph, the system maintains high measurement precision while minimizing memory resource consumption.
Data Source
AI summary
Apparatuses, systems, and techniques to identify a location in which to place objects within a graphically rendered scene. In at least one embodiment, a location in which to place objects is identified using one or more neural networks, based, at least in part, on text or speech input to the one or more neural networks.