6-DoF Stylus Pose Estimation With Camera-IMU Neural Tracking
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Solution Overview
Problem
Existing 3D display systems lack accurate six-degree of freedom (6-DoF) pose estimation for styluses, particularly in interactive augmented reality (AR) and virtual reality (VR) experiences, which hinders precise interaction with virtual objects.
Innovation Solution
Implementing a neural network model and/or a dataset-based model for 6-DoF pose estimation of a stylus, combined with an Inertial Measurement Unit (IMU) and integrated cameras, to determine the stylus's position and orientation in 3D space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D display systems are used without advanced pose estimation models, then the system complexity remains low, but the measurement precision of stylus pose is insufficient
Solution Approach 1:
The pose estimation system is divided into multiple independent modules: IMU module for motion sensing, camera module for visual tracking, and separate neural network/dataset-based models for processing. This segmentation allows each module to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces intermediary processing layers including neural network models and dataset-based estimation models that act as mediators between raw sensor data and final pose estimation. These intermediaries enhance measurement precision by processing and filtering data before final interpretation.
2Measurement precision
If multiple sensing technologies (IMU, cameras, neural networks) are integrated for pose estimation, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The patent merges multiple sensing technologies (IMU, cameras) and processing approaches (neural network models, dataset-based models) into a unified pose estimation system. This combination allows the system to leverage the strengths of each component while achieving accurate 6-DoF pose estimation through data fusion.
Solution Approach 2:
The integrated system serves multiple functions: motion tracking via IMU, visual recognition via cameras, and pose estimation through multiple algorithmic approaches. This multi-functionality allows a single system to handle various sensing and processing tasks, reducing the need for separate dedicated systems.
3Measurement precision
If advanced models (neural network, dataset-based) are implemented for pose estimation, then the interaction precision with virtual objects improves, but the computational energy consumption increases
Solution Approach 1:
The system implements multiple estimation models (neural network, dataset-based) that can be selectively applied based on computational resources available. Not all models need to run simultaneously or at full capacity, allowing the system to achieve sufficient precision while managing energy consumption through selective activation of processing components.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables precise and accurate interaction with virtual objects by estimating the stylus's 6-DoF pose, enhancing the user's experience in AR and VR environments.
Implementation Method 1
The user input device may determine, via an inertial measurement unit (IMU), motion of the user input device in three-dimensional (3D) space
Implementation Method 2
a user input device may capture, e.g., via at least one camera of the user input device, images in a direction that the user input device is directed
Data Source
AI summary
Systems and methods for six-degree of freedom (6-DoF) pose estimation of a user input device, e.g., in a three-dimensional (3D) system rendering interactive augmented reality (AR) and/or virtual reality (VR) experiences include the user input device capturing, via a camera disposed at a forward-facing tip of the user input device, images in a direction the user input device is directed and providing the images to a computer system. The user input device provides inertial measurement unit (IMU) data to the computer system as well. The computer system may then determine pose information associated with the user input device based on the images and IMU data of the user input device. The determination of the pose information may be via usage of at least one of a neural network model, estimation model trained on a set of unique and identifiable patterns, and/or an estimation model trained on a dataset of images.