Neural Light Field 3D Modeling for Faster Mobile Rendering
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Solution Overview
Problem
Neural radiance field (NeRF) networks consume excessive resources and operate inefficiently, leading to delayed rendering times and degraded user experiences, especially on resource-constrained devices like mobile devices, due to their need for multiple iterations and network forwards to estimate radiance along camera rays.
Innovation Solution
Implementing a neural light field (NeLF) network that maps ray origin and direction directly to RGB or pixel values, reducing the need for sampling multiple points and network forwards, allowing for a single forward pass to estimate pixel values, thus enhancing speed and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NeRF networks are used for 3D reconstruction, then radiance estimation accuracy is improved, but rendering time increases and resource consumption increases
Solution Approach 1:
The patent segments the continuous radiance estimation problem into discrete view synthesis tasks. Instead of estimating radiance at every point along camera rays through multiple iterations, the system synthesizes intermediate views from a sparse set of input images, achieving accurate radiance estimation with a single network forward pass by leveraging the segmented view synthesis approach
Solution Approach 2:
The patent performs preliminary action by pre-processing input images to extract features and pre-computing geometric relationships between views before rendering. This allows the network to directly synthesize output views without requiring multiple iterative radiance estimation passes during the actual rendering process, significantly reducing rendering time while maintaining accuracy
2Measurement precision
If NeRF networks are used for 3D reconstruction, then radiance estimation accuracy is improved, but device resource consumption increases
Solution Approach 1:
The patent segments the computationally intensive radiance estimation into a single view synthesis operation. By dividing the problem into sparse view sampling and single-pass synthesis, the system achieves accurate radiance estimation without the multiple iterative passes that consume excessive device resources
Solution Approach 2:
The patent uses copying by synthesizing virtual views from real input images through the neural network. Instead of performing expensive radiance estimation at every pixel location, the system copies and transforms information from sparse input views to generate dense output views, significantly reducing computational resource consumption on mobile devices
3Measurement precision
If multiple network forwards are performed for radiance estimation, then rendering accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent performs preliminary feature extraction and geometric computation before the network forward pass. This preprocessing allows the single network forward pass to directly produce accurate rendered views without requiring multiple iterative passes, thereby maintaining high rendering accuracy while achieving fast processing speed suitable for real-time applications
Data Source
AI summary
Methods and systems are disclosed for performing operations for generating a 3D model of a scene. The operations include: receiving a set of two-dimensional (2D) images representing a first view of a real-world environment; applying a machine learning model comprising a neural light field network to the set of 2D images to predict pixel values of a target image representing a second view of the real-world environment, the machine learning model being trained to map a ray origin and direction directly to a given pixel value; and generating a three-dimensional (3D) model of the real-world environment based on the set of 2D images and the predicted target image.