NeRF View Synthesis via Sparse Volume Data Structure
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Solution Overview
Problem
Neural Radiance Fields (NeRF) based 3D scene reconstruction technologies face high computational requirements and memory consumption, making real-time applications challenging, especially on mobile devices due to the need for millions of inference calculations and significant memory usage for rendering images.
Innovation Solution
The method transforms a NeRF model into a sparse volume data structure by performing spectral analysis and decomposing viewing direction-related color changes into diffused and high-frequency components, using a residual neural network to model color residuals, and representing the 3D space with non-uniformly sized blocks, allowing for efficient rendering on mobile devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NeRF model is used for 3D scene reconstruction, then rendering quality is improved, but computational requirements and memory consumption increase significantly
Solution Approach 1:
The patent divides the 3D scene into discrete voxels and represents the radiance field as a collection of voxel-based volume densities and colors. This segmentation allows the continuous NeRF representation to be discretized into manageable units that can be processed more efficiently, reducing computational complexity while maintaining rendering quality.
Solution Approach 2:
The patent transforms the NeRF model parameters into a sparse volume data structure by changing the representation from continuous neural network weights to discrete voxel attributes (volume density, color, position). This parameter transformation enables the model to run on mobile devices with limited computational resources while preserving the essential 3D scene reconstruction capabilities.
2Measurement precision
If NeRF model is used for 3D scene reconstruction, then rendering quality is improved, but memory consumption increases to 6 Gigabits
Solution Approach 1:
The patent extracts only the essential components needed for rendering from the full NeRF model, storing volume densities and colors only for voxels that contribute to the final image. This extraction approach eliminates redundant data storage while maintaining rendering quality, reducing memory consumption from 6 Gigabits to under 100 megabytes.
Solution Approach 2:
The patent applies different levels of detail to different regions of the 3D space by using adaptive voxel sampling. Regions with higher visual importance or greater complexity receive denser voxel sampling, while less important regions use coarser sampling. This local quality adjustment optimizes memory usage by allocating storage resources where they are most needed.
3Adaptability or versatility
If NeRF model is used for real-time rendering, then view synthesis capability is improved, but processing time exceeds 10 minutes per image
Solution Approach 1:
The patent performs preliminary processing by pre-computing and storing voxel volume densities and colors during an offline training phase. This preliminary action allows the runtime rendering to simply query pre-computed data rather than performing complex neural network inferences, reducing processing time from 10 minutes to under 30 seconds per image while maintaining view synthesis capability.
4Quantity of substance
If sparse volume data structure is used, then memory consumption is reduced to 100 megabytes, but computational complexity increases
Solution Approach 1:
The patent replaces the complex neural network inference mechanism with a simpler voxel querying and composition mechanism. Instead of performing millions of neural network calculations, the system simply retrieves pre-stored voxel data and composes the final image through ray marching and alpha blending operations, significantly reducing computational complexity while maintaining low memory consumption.
Data Source
AI summary
A computer-implemented method for transforming a neural radiance field model is described. A plurality of inputs are provided to a neural radiance field (NeRF) model that represents a 3-dimensional space having a subject, wherein each input of the plurality of inputs includes a location and a view direction and corresponds to respective colors of voxels that represent the 3-dimensional space. A spectral analysis is performed on a plurality of outputs of the NeRF model based on the plurality of inputs, wherein the plurality of outputs include the respective colors of the voxels. Frequency components of the spectral analysis that represent colors for at least some of the voxels are extracted. A sparse volume data structure that represents the 3-dimensional space and the respective colors for the at least some of the voxels is generated.