Real-Time Object Digitization Using Depth Sensing Point Clouds
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Solution Overview
Problem
Current motion capture techniques are costly, application-specific, and fail to incorporate actual player or user motions, as well as environmental concepts, lacking real-time digitization and tracking of objects in a physical space.
Innovation Solution
The use of target digitization techniques that identify and track objects by analyzing point clouds from depth sensing devices, extracting surfaces, textures, and object properties, increasing confidence over time, and providing real-time feedback, enabling the creation of three-dimensional models and real-time tracking of objects, including humans and background elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If typical motion capture techniques are used, then motion data can be captured for avatar animation, but the system is costly and tied to specific applications without capturing actual user motions or environmental objects
Solution Approach 1:
The patent uses depth sensing devices to capture and digitize physical objects, creating digital copies that can be tracked and manipulated in virtual environments. This eliminates the need for expensive motion capture suits and studios by copying object geometry and appearance directly from the physical world using affordable depth cameras.
Solution Approach 2:
The system captures not only motion data but also environmental objects, surfaces, and textures in real-time, making it applicable to multiple scenarios including gaming, virtual reality, and augmented reality. This universal approach replaces application-specific motion capture systems with a multi-functional depth sensing platform.
2Measurement precision
If brute force techniques are used for object detection, then objects can be identified, but real-time digitization and tracking of multiple objects with high fidelity is not achieved
Solution Approach 1:
The system performs preliminary digitization of objects by capturing their geometry, surfaces, and textures in advance and storing them as digital models. When objects move or interact, the system tracks these pre-digitized models rather than detecting objects from scratch, enabling real-time processing with high precision.
Solution Approach 2:
The system continuously tracks objects across multiple frames by comparing their current positions with previously captured positions, providing real-time feedback on object movement and maintaining accurate tracking. This feedback mechanism allows the system to maintain high measurement precision while operating in real-time.
Data Source
AI summary
Techniques may comprise identifying surfaces, textures, and object dimensions from unorganized point clouds derived from a capture device, such as a depth sensing device. Employing target digitization may comprise surface extraction, identifying points in a point cloud, labeling surfaces, computing object properties, tracking changes in object properties over time, and increasing confidence in the object boundaries and identity as additional frames are captured. If the point cloud data includes an object, a model of the object may be generated. Feedback of the model associated with a particular object may be generated and provided real time to the user. Further, the model of the object may be tracked in response to any movement of the object in the physical space such that the model may be adjusted to mimic changes or movement of the object, or increase the fidelity of the target's characteristics.