Multicore Neural Rendering System for Real-Time High-Frequency Detail
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Solution Overview
Problem
Current hardware solutions are not suitable for real-time or near real-time neural radiance field-based image rendering applications, as they are primarily designed for accelerating convolutional neural networks and not optimized for neural networks encoded with neural radiance fields.
Innovation Solution
A multicore computing system is configured with a multilayer perceptron rendering unit and a convolutional neural network rendering unit, where the multilayer perceptron generates a feature map and the convolutional neural network enhances it to produce high-quality images with high-frequency features, addressing the limitations of existing solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current hardware accelerators or processors designed for convolutional neural networks are used, then neural network computations can be accelerated, but they are not suitable for accelerating neural networks encoded with neural radiance fields
Solution Approach 1:
The patent changes the architectural parameters of the processing units from CNN-optimized designs to MLP-optimized designs, modifying the computational structure to handle the different mathematical operations required by neural radiance fields. This includes changing from convolutional operation structures to fully connected layer structures that can process the coordinate-based queries typical of NeRF applications.
Solution Approach 2:
The system segments the rendering task into two distinct processing stages: an MLP-based feature extraction stage for generating base color and density values, and a CNN-based refinement stage for enhancing high-frequency details. This segmentation allows each processing unit to be optimized for its specific function while working together to solve the overall rendering problem.
2Manufacturing precision
If neural radiance field-based image rendering is performed, then photorealistic images from novel viewpoints can be generated, but the rendering process becomes computationally taxing
Solution Approach 1:
The rendering pipeline is segmented into two sequential processing stages: coarse feature extraction using MLP and fine detail refinement using CNN. This segmentation allows the system to first quickly generate a base rendering with acceptable quality, then selectively enhance specific regions requiring high-frequency detail, thereby improving overall rendering speed without sacrificing photorealistic quality.
Solution Approach 2:
The system applies full MLP processing to all pixels for base rendering, but applies CNN refinement selectively only to regions requiring high-frequency enhancement. This partial action approach avoids the excessive computational cost of applying full high-quality processing to every pixel while maintaining photorealistic quality where needed.
3Device complexity
If a single machine learning model is used for rendering, then the system is simpler, but it cannot simultaneously achieve both coarse feature extraction and high-frequency detail rendering
Solution Approach 1:
The single rendering task is segmented into two specialized processing units: an MLP-based unit for coarse feature extraction and a CNN-based unit for high-frequency detail enhancement. This segmentation increases device complexity but enables each unit to be optimized for its specific function, achieving rendering quality that neither unit could achieve alone.
Solution Approach 2:
The patent merges two different machine learning architectures (MLP and CNN) into a unified rendering pipeline where they work sequentially. The MLP output serves as input to the CNN, combining the strengths of both architectures to achieve both efficient coarse rendering and high-quality fine detail enhancement in a single integrated system.
Data Source
AI summary
An image rendering system comprising a preprocessing unit coupled to a feature extract unit and a color rendering unit over a data bus. The preprocessing unit generates vector representations of spatial coordinates of sample points along camera rays corresponding to pixels of an image to be rendered. The feature extract unit generates a feature map of the image based on the vector representations, color and intensity values of the sample point through a first machine learning model. The color rendering unit renders the image based on the feature map through a second machine learning model. The first machine learning model is different from the second machine learning model.