Panoptic Segmentation Forecasting for AR Motion Lag
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Solution Overview
Problem
In augmented reality applications, virtual elements lag behind moving real-world objects due to inadequate motion prediction models, which disrupt the seamless integration of virtual and real environments, especially with faster-moving objects like cars.
Innovation Solution
Panoptic segmentation forecasting, which separates and forecasts the future positions of foreground and background objects using classification models, motion models, and depth estimation, allowing for accurate overlay of virtual elements on real-world environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single motion model is used for all objects in the scene, then the device complexity is reduced, but the measurement precision of object motion forecasting deteriorates
Solution Approach 1:
The patent segments the scene into foreground objects and background, applying different motion models to each. The foreground motion model handles moving objects with unique motion patterns, while the background motion model processes stationary or slowly moving background elements. This segmentation resolves the contradiction by allowing high precision through differentiated modeling while managing complexity through structured division of the scene.
Solution Approach 2:
The system dynamically adapts the motion model based on object characteristics and scene context. By classifying objects as foreground or background and selecting appropriate motion models accordingly, the system achieves dynamic optimization of forecasting precision without requiring a single overly complex universal model.
2Reliability
If motion prediction is applied to all objects, then the seamless integration of virtual and real environments is improved, but the loss of computational time increases
Solution Approach 1:
The patent segments processing into two parallel streams: foreground object motion prediction and background motion prediction. This allows the system to apply computationally intensive prediction algorithms selectively to foreground objects that require high precision, while using optimized methods for background elements, thereby reducing overall computational time while maintaining integration quality.
Solution Approach 2:
Different levels of motion prediction quality are applied to different regions of the scene. Foreground objects receive high-precision motion forecasting with detailed tracking, while background elements receive optimized prediction with appropriate precision for their role. This local differentiation maintains AR integration quality where needed while reducing computational overhead in less critical areas.
Data Source
AI summary
Panoptic segmentation forecasting predicts future positions of foreground objects and background objects separately. An egomotion model may be implemented to estimate egomotion of the camera. Pixels in frames of captured video are classified between foreground and background. The foreground pixels are grouped into foreground objects. A foreground motion model forecasts motion of the foreground objects to a future timestamp. A background motion model backprojects the background pixels into point clouds in a three-dimensional space. The background motion model predicts future positions of the point clouds based on egomotion. The background motion model may further generate novel point clouds to fill in occluded space. With the predicted future positions, the foreground objects and the background pixels are combined into a single panoptic segmentation forecast. An augmented reality mobile game may utilize the panoptic segmentation forecast to accurately portray movement of virtual elements in relation to the real-world environment.