Neural Rendering Model for Shared Event 3D Views
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Solution Overview
Problem
Existing systems fail to effectively utilize available data to generate a temporal-based 3D representation of shared experiences or events in multi-user environments, such as concerts or parties, limiting user interaction and perspective options.
Innovation Solution
The implementation of a neural rendering model, like a Neural Radiance Field (NeRF), that generates and renders views of recorded events from multiple perspectives by querying sensor data from multiple devices, with expiration values to optimize rendering efficiency by reducing unnecessary queries, especially in static or unchanged scenes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a neural rendering model queries sensor data from multiple devices to generate temporal-based 3D representations, then the quality and perspective options of the rendered event views are improved, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by capturing and storing sensor data from multiple devices during the event, then reconstructing the 3D representation offline or in advance. This allows the complex rendering to be done once with stored data rather than querying in real-time, reducing computational complexity during actual viewing.
Solution Approach 2:
The system creates a digital copy (3D representation) of the event from sensor data captured by multiple devices. Instead of processing all raw sensor data continuously, the system queries this pre-generated 3D model for specific viewpoints and timepoints, significantly reducing computational complexity while maintaining view quality.
2Adaptability or versatility
If the system provides multiple perspective views at different timepoints, then the versatility and user interaction options are improved, but the data processing requirements and energy consumption increase
Solution Approach 1:
The system segments the event into discrete timepoints and queries the 3D representation at specific timepoints rather than continuously. This allows the system to provide multiple perspective views on demand while reducing energy consumption by only processing data when views are actually requested, not continuously.
Solution Approach 2:
The system dynamically queries the 3D representation based on user needs and event progress. Instead of continuously processing all sensor data, the system adapts its queries to request only the specific perspectives and timepoints needed, reducing overall energy consumption while maintaining versatility.
3Measurement precision
If the system queries the volume rendering model frequently for dynamic scenes, then the rendering accuracy is improved, but the number of queries and processing time increase
Solution Approach 1:
The system performs preliminary rendering queries to establish initial 3D representations and expiration values. By pre-computing these values, the system reduces the frequency of subsequent queries while maintaining rendering accuracy, thereby improving productivity.
Solution Approach 2:
The system uses feedback from initial queries to determine expiration values, which indicate when rendered views become invalid. This feedback mechanism allows the system to query the volume rendering model only when necessary (when views expire or scenes change), reducing query frequency while maintaining accuracy and improving rendering speed.
Data Source
AI summary
Various implementations record a shared event by generating a temporal-based three-dimensional (3D) representation for use in providing/rendering views of the recorded event. For example, a method may include determining that a second device is currently at an event at a physical environment. The method may further include providing a notification to the second device for an option to authorize use of sensor data based on determining that the second device is currently at the event. The method may further include receiving authorization to use the sensor data. The method may further include obtaining the sensor data from the second device in accordance with the authorization. The method may further include generating a temporal-based 3D representation of the event based on the sensor data obtained from the second device and additional sensor data obtained from one or more other devices at the event.