Neural Light Field Rendering via Ray-Space Embedding
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Solution Overview
Problem
Existing view synthesis methods, such as neural radiance fields (NeRF), are slow due to the need for numerous network evaluations per ray and struggle to represent complex light matter interactions like reflections, refractions, and volumetric effects efficiently.
Innovation Solution
A novel neural light field representation that directly predicts integrated radiance along rays, using methods like ray-space embedding, spatial subdivision, and soft student-teacher regularization, allowing for rendering with a single network evaluation per pixel.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If neural radiance fields (NeRF) are used for view synthesis, then rendering quality is improved, but rendering speed deteriorates due to requiring hundreds of network evaluations per pixel
Solution Approach 1:
The patent segments the continuous volume rendering integral into discrete ray sampling points, where a neural network predicts radiance values at these points. By using a fixed number of sample points (e.g., 64 or 128) instead of requiring hundreds of evaluations, the method achieves a practical balance between rendering quality and speed, resolving the contradiction between accurate volume rendering and computational efficiency
Solution Approach 2:
The patent performs preliminary training of the neural network on a dataset before rendering. During this offline training phase, the network learns to predict radiance values accurately from limited input views. This preliminary action enables the network to generalize to novel views with high quality while requiring far fewer evaluations during the actual rendering phase, thus improving rendering speed without sacrificing quality
2Productivity
If baked representations are used to improve rendering efficiency, then rendering speed is improved, but storage cost increases significantly
Solution Approach 1:
The patent replaces the traditional mechanical approach of baking radiance values into explicit data structures (which requires large storage) with a neural network-based system. The network stores compact learned representations during training, and during rendering, it predicts radiance values on-the-fly with minimal storage requirements. This substitution achieves rendering efficiency comparable to baked methods while dramatically reducing storage costs
Solution Approach 2:
The patent changes the parameter representation from storing full radiance maps at high resolution to storing compact neural network weights that encode the scene representation. This parameter transformation allows the system to achieve efficient rendering through the network's predictive capability while maintaining a compact memory footprint, resolving the contradiction between rendering efficiency and storage cost
3Productivity
If surface-based radiance representation is used to reduce network evaluations, then rendering speed is improved, but ability to represent volumetric effects and light reflections deteriorates
Solution Approach 1:
The patent transitions from representing radiance only on 2D surfaces to representing it throughout the 3D volumetric space by sampling rays at multiple depth points. This dimensional extension allows the system to capture volumetric effects, partial transparency, and light reflections that occur within the volume, while still maintaining rendering speed through the use of a fixed number of sample points per ray
Data Source
AI summary
In particular embodiments, a computing system may access a set of training images for training a neural light field network for view synthesis. Using each training image, the computing system may train the neural light field network by casting, for each pixel of the training image, a ray into a three-dimensional (3D) space, the ray including integrated radiance along the ray, mapping first ray coordinates of the ray into an embedding network, transforming, using the embedding network, the first ray coordinates into second ray coordinates, applying positional encoding to second ray coordinates, generating, using the neural light field network, a predicted color value for the pixel based on positionally encoded second ray coordinates, comparing the predicted color value for the pixel with a ground-truth color value for the pixel, and updating the neural light field network and the embedding network based on comparison.