3D Volumetric Capture for Real-Time Item Identification in Live Events
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Solution Overview
Problem
Viewers face challenges in identifying and researching items shown during live events due to limited information and fast-paced environments, making it difficult to capture relevant details about objects on screen.
Innovation Solution
A 3D volumetric capture system using depth and color data from multiple perspectives constructs a 3D mesh, which is processed by a machine learning model to generate textual descriptions of items, enabling real-time information retrieval through virtual or augmented reality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional 2D video broadcasting is used, then viewers can watch live events, but they cannot obtain sufficient information about items displayed on screen
Solution Approach 1:
The patent transitions from traditional 2D video broadcasting to 3D volumetric capture, adding a spatial dimension to the visual representation. This enables viewers to see items from multiple angles and obtain comprehensive information about displayed objects, directly addressing the information loss problem while managing system complexity through structured data capture.
Solution Approach 2:
The system introduces an intermediary processing layer that captures volumetric data, generates 3D meshes, and creates detailed item descriptions. This intermediary processing pipeline acts as a mediator between the live event and the viewer, enriching the information available about displayed items without requiring direct access to physical objects.
2Measurement precision
If viewers try to capture item details during fast-paced live events, then they may identify some items, but the fast pace and limited time make accurate identification difficult
Solution Approach 1:
The system performs preliminary actions by pre-capturing volumetric data and pre-generating 3D meshes and item descriptions before the viewer needs the information. This allows the system to have item information ready in advance, eliminating the time pressure that viewers face during fast-paced events and enabling accurate identification without time loss.
Solution Approach 2:
The system implements feedback mechanisms where captured volumetric data is continuously processed to generate real-time 3D representations and item descriptions. This feedback loop provides viewers with accurate item information as it becomes available, maintaining measurement precision while reducing the effective time loss by providing information as soon as it is captured and processed.
3Loss of information
If multiple cameras and processing systems are deployed to capture item details, then item information can be obtained, but system complexity increases
Solution Approach 1:
The system segments the complex capture and processing task into distinct modular components: volumetric capture modules, 3D mesh generation modules, and item description generation modules. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity while maintaining comprehensive item information capture.
Solution Approach 2:
The volumetric capture system is designed with multi-functionality, serving as a universal platform that can capture various types of items (clothing, accessories, props) from multiple angles simultaneously. This universal approach reduces the need for specialized equipment for different item types, managing device complexity while comprehensively capturing item detail information.
Data Source
AI summary
Disclosed are systems and methods that leverage machine learning techniques to provide information about items that are on display during a live event. A 3D volumetric capture system is used to construct a 3D representation of the event in real time. For example, depth and color data from multiple perspectives may be used to construct a 3D mesh of colored polygons that represent the scene. Users may view the live event with a 3D enabled virtual reality or augmented reality headset. Background subtraction may be used to isolate items of interest. In some configurations a machine learning model may process the 3D mesh to generate a textual description of items in the scene. The textual description may then be used to search for more information about a particular item. Additionally, or alternatively, the machine learning model may directly predict one or more items in the scene.