High-Resolution Neural Rendering With Cached MLPs
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Solution Overview
Problem
Neural Radiance Fields (NeRF) methods for rendering 3D scenes are computationally expensive due to the need for numerous neural network calls for each pixel, making the process slow and inefficient.
Innovation Solution
Separate the NeRF neural network into two cached MLP networks for positional and directional data, allowing efficient inference by caching neural network outputs for a wide range of inputs and replacing network execution with cache look-ups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single neural network is used to process both positional and directional data in NeRF, then the model structure is simple, but the computational cost and processing time increase significantly
Solution Approach 1:
The patent divides the single neural network into two separate neural networks: one for processing positional data and another for processing directional data. This segmentation allows each network to specialize in specific computations, reducing the computational burden on any single network and enabling more efficient rendering by processing positional and directional components independently and in parallel
2Measurement precision
If neural network calls are made for every pixel to achieve fine details, then rendering accuracy is improved, but processing time and computational expense increase
Solution Approach 1:
The patent pre-computes and caches the outputs of both positional and directional neural networks for various input configurations. By performing these computations in advance and storing them in a cache data structure, the system avoids redundant neural network calls during actual rendering, significantly reducing processing time while maintaining rendering accuracy through the cached pre-computed values
3Device complexity
If a single neural network processes all data, then the system is simpler to implement, but memory requirements and storage needs increase
Solution Approach 1:
The patent segments the neural network computations into two separate networks with specialized functions, which allows for more efficient memory management. Each network processes and stores only the specific type of data it handles (positional or directional), reducing the overall memory footprint compared to a single network that would need to store and process both types of data simultaneously
Data Source
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AI summary
Methods and systems are provided for training a machine learning model to generate density values and radiance components based on positional data, along with a weighting scheme associated with a particular view direction based on directional data to compute a final RGB value for each point along a plurality of camera rays. The positional data and directional data are extracted from set of training images of a particular static scene. The radiance components, density values, and weighting schemes are cached for efficient image data processing to perform volume rendering for each point sampled. A novel viewpoint of a static scene is generated based on the volume rendering for each point sampled.