AR Digital Object Matching for Context-Aware 3D 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 without manual user intervention.
Innovation Solution
An MR context solver system that automatically determines the optimal placement of digital objects in mixed reality environments by analyzing real-world data and matching conditions associated with digital objects to segments of the environment, allowing for efficient and automated spawning of digital content without manual user placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual authoring techniques are used to place virtual objects in mixed reality environments, then the author has control over object placement, but the process becomes time-consuming and inefficient due to the dynamic and unpredictable nature of real-world objects
Solution Approach 1:
The system enables self-service by allowing the MR environment to automatically determine optimal placement locations for digital objects based on detected real-world surfaces and contexts. The environment itself provides the placement information through surface detection and context analysis, eliminating the need for manual authoring intervention while maintaining appropriate object placement.
Solution Approach 2:
The system performs preliminary action by pre-detecting and cataloging suitable placement surfaces and contexts in the real-world environment before digital objects need to be placed. The surface detection and context analysis are completed in advance, creating a ready pool of valid placement locations that can be quickly assigned to digital objects without manual intervention.
2Manufacturing precision
If manual visual determination is used to place virtual objects on detected surfaces, then the author can ensure appropriate placement, but the process lacks efficiency due to incomplete knowledge of the runtime environment during authoring
Solution Approach 1:
The system applies dynamics by transitioning from static pre-authoring placement to dynamic runtime placement determination. The system detects real-world surfaces and contexts at runtime, allowing placement decisions to be made based on actual environmental conditions rather than assumptions made during authoring. This dynamic approach maintains placement accuracy while dramatically improving authoring speed.
Solution Approach 2:
The system replaces the mechanical manual visual determination process with an automated computer vision and context analysis system. Instead of requiring the author to visually inspect and manually place objects, the system uses surface detection algorithms and context analysis to automatically identify suitable placement locations, maintaining accuracy while eliminating manual effort.
3Adaptability or versatility
If only basic horizontal/vertical planes are provided for object placement, then the system keeps complexity low, but the system lacks contextual understanding of what surfaces represent (e.g., floor vs. tabletop)
Solution Approach 1:
The system applies segmentation by dividing the detected real-world environment into distinct contextual segments such as floors, walls, tabletops, and other surface types. Each segment is characterized by specific properties that determine its suitability for different types of digital object placement. This segmentation enables contextual understanding while managing complexity through structured categorization.
Solution Approach 2:
The system uses parameter changes by analyzing multiple surface properties (orientation, position, size, normal vectors) to transform basic geometric plane detection into contextual surface identification. By changing from simple plane detection parameters to multi-parameter surface characterization, the system gains contextual understanding of what surfaces represent without excessive complexity increase.
Data Source
AI summary
A method of spawning a digital island in a three-dimensional environment is disclosed. Data describing a three-dimensional environment is accessed. The data is partitioned into a plurality of contexts based on properties identified in the data, the properties corresponding to surfaces or objects in the three-dimensional environment. One or more values of one or more traits corresponding to a context of the plurality of context are identified. A digital island is matched to the context. The matching includes analyzing one or more conditions associated with the digital island with respect to the one or more values of the one or more traits corresponding to the context. Based on the matching, the spawning of the digital island is performed in the three-dimensional environment for the context.