Neural Network 2D to 3D Volumetric Conversion for Heterogeneous Streaming
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Solution Overview
Problem
Current technologies lack a coherent end-to-end ecosystem for distributing immersive media over commercial networks due to the lack of a single standard representation, heterogeneity of immersive media devices, and the inability to support real-time conversion of natural content into synthetic media formats suitable for diverse client end-points.
Innovation Solution
A network-based media distribution system that adapts input immersive media sources into formats suitable for specific client end-point devices using neural networks, converting 2D media into volumetric representations to accommodate heterogeneous devices, including legacy and emerging immersive media displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If 2D video content is converted to 3D volumetric format using neural networks, then compatibility with immersive media displays is improved, but processing time and computational complexity increase
Solution Approach 1:
The system performs preliminary conversion of 2D video content to 3D volumetric format using pre-trained neural network models before distribution. This advance preparation allows immersive media displays to receive pre-processed content in compatible formats, reducing real-time processing requirements and enabling faster playback without compromising compatibility.
Solution Approach 2:
The neural network creates synthetic 3D volumetric representations as copies of the original 2D video content. These synthesized 3D copies maintain the essential visual information while adding depth and spatial characteristics needed for immersive displays, allowing the system to support multiple display types without requiring separate content creation for each format.
2Productivity
If a single network distributes both legacy and immersive media, then network utilization and resource sharing are improved, but system complexity and protocol requirements increase
Solution Approach 1:
The network system is designed with universal protocols that can handle both legacy 2D video streams and immersive 3D volumetric content through a single distribution infrastructure. The adaptation process unit and neural network converters enable the same network to serve multiple types of client devices (legacy displays, VR headsets, AR glasses, holographic displays) without requiring separate dedicated networks, thereby improving resource utilization while managing complexity through standardized interfaces.
3Speed
If 2D video is converted to 3D format in real-time, then responsiveness to client requests is improved, but processing load and computational resources required increase
Solution Approach 1:
The system introduces an adaptation process unit as an intermediary component that manages the conversion between 2D and 3D formats. This intermediary layer includes neural network models that can operate in real-time or near-real-time, bridging the gap between legacy 2D content sources and immersive 3D displays. The intermediary handles the computational burden of conversion, allowing client devices to receive processed content without requiring excessive local processing power.
Data Source
AI summary
A method, a computer program, and a computer system are provided for streaming immersive media. The method, the computer program, and computer system includes ingesting content in a two-dimensional format; converting the ingested content to a three-dimensional format based on a neural network; and streaming the converted content to a client end-point.