Dynamic Model Selection for XR Environment State
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Solution Overview
Problem
Generating extended reality (XR) environments that evolve over time is computationally intensive, especially when modeling across larger timescales, as existing methods require extensive computation to accurately simulate and update asset states.
Innovation Solution
Implementing a system that uses multiple models to determine environment states at different timescales, allowing for efficient computation by selecting the appropriate model based on the timestep, with more computationally efficient models used for larger time increments, such as the second model for long periods where certain asset states become irrelevant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single detailed model is used to simulate asset states over time, then accuracy of environment state determination is improved, but computational burden increases significantly
Solution Approach 1:
The system dynamically selects between different models (first model for short timesteps, second model for long timesteps) based on the time increment required. This dynamic adaptation allows the system to maintain accuracy when needed while reducing computational burden for longer simulations, directly resolving the contradiction between precision and computational cost.
Solution Approach 2:
The system changes the model selection parameter based on the timestep value. When the timestep exceeds a threshold, the system switches from the first detailed model to the second computationally efficient model. This parameter-based model selection enables the system to adjust its computational approach according to the required precision and time scale.
2Productivity
If multiple models are used to handle different timescales, then computational efficiency is improved, but device complexity increases
Solution Approach 1:
The system segments the modeling task into two distinct models: a first model for short timesteps and a second model for long timesteps. Each model is optimized for its specific timescale, allowing the system to achieve computational efficiency for long-term simulations while maintaining accuracy for short-term updates. This segmentation resolves the contradiction by dividing the complex problem into manageable parts.
Solution Approach 2:
The system uses an intermediary mechanism (model selector) that determines which model to apply based on the timestep parameter. This intermediary layer manages the complexity of having multiple models by providing a clear selection criterion, thereby simplifying the overall system architecture while still benefiting from the efficiency gains of multiple specialized models.
3Measurement precision
If detailed asset states are tracked for long timescales, then accuracy is maintained, but computation time increases excessively
Solution Approach 1:
The system dynamically adjusts its tracking detail based on the timescale. For long timesteps, it uses the second computationally efficient model that tracks essential states without maintaining excessive detail. For short timesteps, it switches to the first detailed model. This dynamic adjustment maintains necessary accuracy while dramatically reducing computation time for long-term simulations.
Solution Approach 2:
The system applies partial tracking for long timesteps using the second model, focusing only on the most critical asset states that evolve over long periods. This partial action approach avoids the excessive computation required to track all detailed states continuously, while still maintaining sufficient accuracy for long-term environment simulation.
Data Source
AI summary
In various implementations, a device surveys a scene and presents, within the scene, a extended reality (XR) environment including one or more assets that evolve over time (e.g., change location or age). Modeling such an XR environment at various timescales can be computationally intensive, particularly when modeling the XR environment over larger timescales. Accordingly, in various implementations, different models are used to determine the environment state of the XR environment when presenting the XR environment at different timescales.