Mixed Reality Condition Generator for Automated Object Placement
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Solution Overview
Problem
Creating and editing digital content in mixed reality environments is challenging due to the dynamic and unpredictable nature of real-world objects, making it difficult to efficiently place, orient, and scale virtual objects during the authoring process, as current MR authoring software often lacks complete knowledge of the runtime environment and can only detect limited horizontal/vertical planes without context.
Innovation Solution
The MR Condition Generator system automatically generates conditions for spawning digital objects by learning from user actions in a known MR environment, allowing for the creation of digital content without programming, by analyzing real-world data to determine optimal placement and properties of virtual objects in a mixed reality environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual authoring techniques are used in mixed reality environments, then authors can place virtual objects with visual inspection, but the process becomes inefficient and time-consuming due to incomplete knowledge of runtime environment
Solution Approach 1:
The system performs preliminary actions by automatically detecting real-world surfaces and objects during runtime, generating semantic information and candidate placement locations before the author needs to place virtual objects. This pre-processing of environment data eliminates the need for manual visual inspection and placement during the authoring process.
Solution Approach 2:
The authoring system serves itself by automatically analyzing runtime environment data, generating semantic information about surfaces and objects, and proposing placement locations without requiring manual author intervention. The system uses its own runtime detection capabilities to populate the authoring environment with contextual information.
2Difficulty of detecting and measuring
If current MR authoring software detects horizontal/vertical planes, then some surfaces are identified, but the detection lacks semantic context (e.g., floor vs. tabletop) limiting placement options
Solution Approach 1:
The system introduces an intermediary layer of semantic information between raw surface detection and virtual object placement. This intermediary layer includes object type classification (floor, tabletop, shelf), semantic properties, and contextual relationships, which are generated by analyzing runtime environment data and bridging the gap between geometric detection and meaningful placement decisions.
Solution Approach 2:
The system replaces manual mechanical inspection and classification by authors with automated computational analysis of runtime environment data. Machine learning and image processing algorithms substitute for human visual inspection, automatically generating semantic information about surfaces and objects without requiring author intervention.
3Manufacturing precision
If virtual objects are placed manually by visual determination, then placement accuracy can be achieved, but the authoring process lacks automation and scalability
Solution Approach 1:
The system uses feedback from runtime environment detection to automatically adjust and determine optimal placement locations for virtual objects. The runtime detection of real-world surfaces and objects provides feedback that informs the authoring process, allowing automated placement decisions that adapt to the actual environment rather than relying on static pre-authoring specifications.
Solution Approach 2:
The system changes the parameters of the authoring process from static visual inspection to dynamic automated analysis. By transforming environment data parameters (surface geometry, object detection results, semantic information) into placement recommendations, the system enables automated determination of placement accuracy without manual intervention.
Data Source
AI summary
Systems and methods for spawning a digital object in an environment are disclosed. Data describing the environment is received. The data includes data describing properties of the environment, a state of the environment, and properties of a plurality of objects within the environment. The data is analyzed to detect and categorize one or more of the plurality of objects, and to detect one or more surfaces related to the plurality of objects. Data is received that describes a placement of the digital object on one of the detected surfaces or detected objects and determines properties of the placement. Conditions are associated with the placed digital object, the conditions including data describing properties of the placement, data describing properties of the detected object, and data describing a state of the detected object. The spawning of the digital object is performed in the environment based on the conditions.