Real-time Implicit Rendering via Server-side Neural Network Offloading
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Solution Overview
Problem
Real-time rendering of high-quality three-dimensional graphical content using implicit rendering techniques is challenging due to the high processing power required, which is beyond the capabilities of personal computing devices like smartphones and laptops.
Innovation Solution
Offloading the computationally expensive process of executing a trained neural network for implicit rendering to powerful servers, which generate pre-computed color and density values, and storing these values in the device's memory for efficient rendering using volumetric techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If implicit rendering techniques using trained neural networks are used to generate high-quality three-dimensional graphical content, then image quality is improved, but processing power requirements increase beyond the capabilities of personal computing devices
Solution Approach 1:
The trained neural network executes the computationally expensive implicit rendering process in advance on powerful servers to generate pre-computed color and density values. These pre-computed results are then transmitted to and stored on personal computing devices, eliminating the need for the devices to perform the heavy neural network execution while still achieving high-quality rendered images.
2Adaptability or versatility
If the trained neural network is executed repeatedly on personal computing devices for real-time rendering, then rendering flexibility is improved, but frame rate smoothness deteriorates due to computational limitations
Solution Approach 1:
Instead of executing the trained neural network directly on personal computing devices, the system creates copies of the pre-computed color and density values generated by server-side neural network execution. These copied values are transmitted to and stored locally on personal devices, enabling real-time rendering through efficient volumetric techniques while maintaining smooth frame rates.
3Extent of automation
If computationally expensive neural network execution is performed on personal computing devices, then rendering autonomy is improved, but processing time increases reducing real-time performance
Solution Approach 1:
The patent extracts the computationally expensive neural network execution process from personal computing devices and relocates it to powerful servers. The servers generate pre-computed color and density values that are then transmitted to personal devices, which only need to perform efficient volumetric rendering operations, dramatically reducing processing time and enabling real-time performance.
Data Source
AI summary
A device for graphical rendering includes a memory and processing circuitry. The processing circuitry is configured to receive sample values, transmitted by one or more servers, of samples of an object, wherein the sample values are generated by the one or more servers from inputting coordinates into a trained neural network and outputting, from the trained neural network, the sample values of the samples, store the sample values in the memory, and render image content of the object based on the sample values.