Stochastic Pruning for Dynamic Level of Detail Model Generation
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Solution Overview
Problem
Current computer animation techniques face challenges in reducing rendering time and memory requirements for complex scenes with many geometric elements, particularly due to the inefficiencies of level of detail (LOD) transitions and manual definition of LOD models, which result in visual artifacts and high computational costs.
Innovation Solution
The introduction of stochastic pruning, a Monte Carlo-type sampling technique that automatically simplifies objects by selecting a subset of geometric elements based on their contribution to pixel appearance, allowing for dynamic creation of reduced complexity models that can be loaded and rendered efficiently, while maintaining image quality and reducing visual artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual definition of level of detail (LOD) models is used, then rendering time can be reduced, but visual artifacts appear and device complexity increases
Solution Approach 1:
The system automatically generates level of detail models by processing the original high-detail model through geometric simplification algorithms, eliminating the need for manual LOD model creation while avoiding visual artifacts. The automatic process analyzes the original model's geometry and generates appropriate simplified versions without human intervention.
Solution Approach 2:
The patent changes the geometric parameters of the model by adjusting the number of polygons and their complexity based on the desired level of detail. This parameter transformation allows the same base model to produce multiple LOD versions with different levels of geometric detail, reducing rendering time without introducing visual artifacts.
2Loss of time
If manual definition of level of detail (LOD) models is used, then rendering time can be reduced, but device complexity increases
Solution Approach 1:
The system automatically generates level of detail models by processing the original high-detail model through geometric simplification algorithms, eliminating the need for manual LOD model creation while avoiding visual artifacts. The automatic process analyzes the original model's geometry and generates appropriate simplified versions without human intervention.
Solution Approach 2:
The patent segments the original high-detail model into multiple level of detail models with progressively simplified geometries. This segmentation allows the rendering system to select appropriate LOD models based on distance and computational requirements, reducing overall device complexity while maintaining rendering performance.
3Manufacturing precision
If high geometric complexity models are used, then image quality is maintained, but rendering time and memory requirements increase
Solution Approach 1:
The system dynamically selects and switches between different level of detail models based on the rendering context, such as object distance and computational budget. This dynamic adaptation allows the system to maintain image quality when needed while significantly reducing rendering time for distant or less critical objects.
Solution Approach 2:
The patent applies different levels of geometric detail to different parts of the scene based on their importance and distance. Close objects and those requiring high image quality use detailed models, while distant objects use simplified LOD models, optimizing the balance between image quality and rendering performance.
Data Source
AI summary
A method for a computer system includes opening a model of an object, wherein the model comprises a plurality of geometric elements, determining a subset of geometric elements from the plurality of geometric elements of the model, modifying properties of one or more of the geometric elements in the subset of geometric elements to form a modified subset of geometric elements, and using the modified subset of geometric elements to represent the model of the object in the computer system.