Object Permanence Modeling for Occlusion-Aware Simulated Environments
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Solution Overview
Problem
Simulated environments in applications like virtual or augmented reality, video games, and robotics struggle to maintain accurate representations of objects over time, especially when objects are occluded or move out of view.
Innovation Solution
A method that uses image sensors and processors to access and manage simulated environments by capturing image data, updating environment models, and determining whether objects are still present or occluded, thereby maintaining or removing their representations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If objects are removed from the environment model when not detected in image data, then the environment model remains clean and up-to-date, but objects that are temporarily occluded are incorrectly removed
Solution Approach 1:
The system performs preliminary actions by maintaining object representations in the environment model even when objects are temporarily not detected in image data. This preliminary maintenance prevents premature removal of objects that may be occluded or temporarily out of view, ensuring object permanence is preserved until conclusive evidence of removal is obtained.
2Loss of information
If all objects are continuously tracked in the environment model, then object permanence is maintained, but computational resources and model complexity increase
Solution Approach 1:
The system applies dynamics by making the environment model adaptive and dynamic rather than static. Objects are maintained, updated, or removed based on their detection status and occlusion state, allowing the model to dynamically adjust its content based on current sensor data and predicted object states, optimizing both resource usage and accuracy.
Solution Approach 2:
The system changes parameters by transitioning object representations between different states (present, occluded, removed) based on detection algorithms and occlusion analysis. This parameter-based state management allows efficient tracking of objects without permanently maintaining all possible object data, reducing overall model complexity while preserving necessary object information.
3Reliability
If occlusion detection is performed to maintain occluded objects, then object permanence is improved, but detection accuracy and processing time are affected
Solution Approach 1:
The system introduces an intermediary occlusion detection mechanism that acts as a mediator between object detection and object removal decisions. This intermediary layer analyzes whether missing objects are likely occluded based on environmental context and sensor data, providing a nuanced decision-making process that improves detection accuracy without requiring exhaustive processing of all possible scenarios.
Data Source
AI summary
Systems, methods, and computer program products for managing simulated environments are described. A simulated environment is accessed which represents a physical environment, and representations of objects are included, maintained, or removed in the simulated environment based on whether objects are represented in image data of the physical environment, and based on whether objects are occluded by other objects in the image data of the physical environment. Future occlusion can also be predicted based on motion paths of objects.