AR Pose Estimation via Depth Sensing and Segmentation
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Solution Overview
Problem
Current augmented and virtual reality systems face challenges in providing seamless social interactions due to limitations in acquiring the necessary granularity of information, leading to inefficiencies in processing time and bandwidth, and difficulties in interfacing with previous generation human-computer interaction systems, especially in sharing experiences across different times and locations.
Innovation Solution
The implementation of advanced methods for accurate mapping and localization, using devices with depth sensors and cameras, to create detailed 3D models of environments, enabling precise pose estimation and tracking, which allows for the integration of virtual objects into real-world contexts without the need for cumbersome markers, and facilitates social interactions across disparate systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced mapping and localization methods are implemented to achieve precise pose estimation, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the complex mapping and localization task into multiple components: depth sensing, feature detection, pose estimation, and virtual object placement. Each component processes specific aspects of the problem independently, improving overall precision while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediate data structures and processing layers between the sensors and the final pose estimation output. These intermediaries (such as feature points, depth maps, and coordinate transformations) facilitate precise measurements without requiring the entire system to be overly complex.
2Manufacturing precision
If detailed 3D models are created using depth sensors and cameras, then manufacturing precision is improved, but loss of energy increases due to processing requirements
Solution Approach 1:
The system creates 3D models with appropriate detail levels based on the specific application requirements. Rather than capturing every possible detail, the system uses selective depth sensing and feature detection to achieve sufficient accuracy while minimizing processing energy consumption.
Solution Approach 2:
The patent dynamically adjusts model detail parameters, resolution levels, and processing intensity based on real-time requirements. When high precision is needed, the system increases detail; when energy efficiency is prioritized, it reduces processing intensity while maintaining acceptable accuracy.
3Adaptability or versatility
If seamless social interactions are enabled across disparate systems, then adaptability is improved, but loss of time increases due to interface compatibility requirements
Solution Approach 1:
The system implements universal communication protocols and standardized data formats that enable different AR/VR systems to interact seamlessly. By creating a common interface layer that can handle multiple device types and platforms, the system achieves broad adaptability without significant time losses during cross-system interactions.
Solution Approach 2:
The patent establishes pre-defined communication protocols, data schemas, and compatibility layers in advance. This preliminary setup allows disparate systems to connect and interact efficiently without requiring time-consuming negotiation or adaptation during actual social interactions.
4Ease of operation
If virtual objects are integrated into real-world contexts without markers, then ease of operation is improved, but reliability decreases due to potential jitter and misplacement
Solution Approach 1:
The system replaces traditional mechanical marker-based tracking with optical and computational methods using depth sensors and cameras. This substitution enables markerless operation while maintaining reliability through advanced image processing, feature detection, and continuous pose estimation algorithms that compensate for potential jitter and misplacement.
Data Source
AI summary
Augmented and virtual reality systems are becoming increasingly popular. Unfortunately, their potential for social interaction is difficult to realize with existing techniques. Various of the disclosed embodiments facilitate social augmented and virtual reality experiences using, e.g., topologies connecting disparate device types, shared-environments, messaging systems, virtual object placements, etc. Some embodiments employ pose-search systems and methods that provide more granular pose determinations than were previously possible. Such granularity may facilitate functionality that would otherwise be difficult or impossible to achieve.