3D Model Node Selection Using Circle of Confusion Threshold
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Solution Overview
Problem
Current systems for rendering 3D models struggle to efficiently manage the level of detail based on the depth of field effect, leading to excessive computational resources when simulating focus and blur, particularly in areas far from the focal plane.
Innovation Solution
A method that selects nodes for rendering 3D models using camera parameters, determines the smallest circle of confusion, and adjusts the threshold error value to replace nodes with coarser resolution when the diameter exceeds the threshold, thereby optimizing data rendering and reducing computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution nodes are used for rendering 3D models, then rendering quality is improved, but computational resources and processing time increase
Solution Approach 1:
The patent applies local quality by rendering different regions of the 3D model at different resolutions based on their importance and visibility. High-resolution nodes are used for focal regions where detail is important, while low-resolution nodes are used for peripheral or out-of-focus regions. This selective approach maintains overall rendering quality while significantly reducing computational resources compared to uniform high-resolution rendering.
Solution Approach 2:
The patent segments the 3D model rendering into multiple nodes with different resolution levels. Each node represents a specific region or aspect of the model that can be independently rendered at an appropriate resolution. This segmentation allows the system to manage computational load by processing only necessary high-resolution regions while using coarser resolution for other areas.
2Reliability
If depth of field simulation is implemented with focus and blur effects, then immersive experience is improved, but computational load increases excessively
Solution Approach 1:
The patent implements depth of field effects by applying focus and blur selectively to different regions rather than uniformly across the entire scene. The focal region receives high-resolution rendering with sharp details, while peripheral regions receive lower-resolution rendering with blur effects. This local differentiation creates an immersive depth of field experience while avoiding the excessive computational load of applying full-resolution rendering and blur effects to all regions.
3Productivity
If the number of nodes for rendering is reduced, then processing power is minimized, but rendering quality deteriorates
Solution Approach 1:
The patent maintains rendering quality with fewer nodes by strategically placing high-resolution nodes only in regions where visual quality is critical (focal regions), while using low-resolution nodes in peripheral regions. This selective quality distribution ensures that the most important visual information is preserved while reducing the total number of nodes and associated processing requirements.
Solution Approach 2:
The patent extracts and prioritizes the most visually important regions of the 3D model for high-resolution rendering. By identifying and isolating focal regions that contribute most to the immersive experience, the system can concentrate computational resources on these extracted regions while using coarser representation for less important areas, thereby maintaining overall quality with reduced node counts.
Data Source
AI summary
A set of nodes for rendering a 3D model using a set of camera parameters based on at least a threshold error value may be selected. Each node corresponds to a bundle of data for rendering a geographic area at a particular resolution and the camera parameters including a point of view for rendering the 3D model. For a given node of the set of nodes, a smallest circle of confusion within a bounding box of the geographic area of the given node may be determined using the set of camera parameters closest to a particular parameter of the set of camera parameters. A diameter of the smallest circle of confusion is compared to the threshold error value. Based on this comparison, the threshold error value is adjusted. The adjusted threshold error value is used to select a new node to replace the given node in the set of nodes.