Constraint-Based Layout Optimizer for Real-Time AR Scene Adaptation
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Solution Overview
Problem
Existing augmented reality technologies face challenges in dynamically and efficiently generating optimized layouts for embedding virtual objects into environments, often requiring extensive computational resources and failing to adapt in real-time to changing environmental conditions.
Innovation Solution
The Layout Optimizer employs a constraint-based framework with iterative methodologies to dynamically generate optimized layouts for virtual objects within environments, using locally satisfiable proposals to minimize global cost and adapt to environmental changes, thereby reducing the number of samples needed and enabling real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sampling algorithms are used to generate layouts for virtual objects, then comprehensive coverage of the solution space is achieved, but the number of samples required becomes extremely large, resulting in excessive computational time and resources
Solution Approach 1:
The patent segments the layout optimization problem into two distinct phases: (1) a sampling phase that generates candidate layouts using traditional algorithms, and (2) a filtering phase that applies constraint-based filtering to identify high-quality layouts. This segmentation allows the system to use computationally expensive sampling methods only when necessary, while relying on efficient filtering for the majority of cases, thereby reducing overall computational time while maintaining accuracy.
Solution Approach 2:
The patent performs preliminary constraint analysis and filtering before final layout selection. By pre-computing constraint violations and filtering out infeasible candidates early in the process, the system avoids wasting computational resources on evaluating invalid layouts, thus reducing the number of samples needed to achieve optimal results.
2Adaptability or versatility
If real-time adaptation to environmental changes is implemented, then the system remains relevant and useful in dynamic conditions, but computational overhead increases, making real-time performance difficult to achieve
Solution Approach 1:
The patent implements a dynamic layout optimization system that adapts to environmental changes in real-time. The constraint-based filtering framework is designed to efficiently re-evaluate layouts when environmental conditions change, using incremental updates rather than complete re-optimization. This allows the system to maintain adaptability while controlling computational overhead through smart caching and selective re-evaluation of constraints.
3Reliability
If extensive sampling is performed to ensure layout consistency with constraints and environment, then layout quality improves, but computational resources and processing time increase significantly
Solution Approach 1:
The patent implements a feedback mechanism where the constraint-based filtering process continuously evaluates layout candidates against environmental constraints and provides feedback for iterative improvement. The system learns from previous evaluations to prioritize promising regions of the solution space, reducing the number of samples needed while maintaining high reliability in constraint satisfaction.
Solution Approach 2:
The patent dynamically adjusts sampling parameters and filtering thresholds based on problem characteristics and computational resource availability. By changing parameters such as sample size, constraint weightings, and filtering strictness, the system can optimize the balance between computational resource usage and layout reliability for different scenarios.
Data Source
AI summary
A “Layout Optimizer” provides various real-time iterative constraint-satisfaction methodologies that use constraint-based frameworks to generate optimized layouts that map or embed virtual objects into environments. The term environment refers to combinations of environmental characteristics, including, but not limited to, 2D or 3D scene geometry or layout, scene colors, patterns, and/or textures, scene illumination, scene heat sources, fixed or moving people, objects or fluids, etc., any of which may evolve or change over time. A set of parameters are specified or selected for each object. Further, the environmental characteristics are determined automatically or specified by users. Relationships between objects and/or the environment derived from constraints associated with objects and the environment are then used to iteratively determine optimized self-consistent and scene-consistent object layouts. This enables the Layout Optimizer to augment environments with arbitrary content in a structured constraint-based process that adapts to changing scenes or environments.