Mobile Neural Light Field Rendering With Partial-Ray Super-Resolution
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Solution Overview
Problem
Neural Rendering Fields (NeRF) are slow and require high-end GPUs or extra storage memory, making them unsuitable for resource-constrained devices like mobile devices, while neural light fields (NeLF) are computationally intensive and not mobile-friendly.
Innovation Solution
The MobileR2L architecture uses a convolutional network instead of MLP, employs super-resolution modules, and renders a light-field volume, reducing memory and latency by forwarding only a partial number of rays for high-resolution image generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NeRF is used for novel view synthesis, then rendering quality is improved, but inference speed becomes extremely slow
Solution Approach 1:
The patent replaces the traditional volumetric rendering mechanism with a neural light field approach that uses a neural network to directly predict pixel colors along rays, eliminating the need for complex ray-tracing through 3D volumes and achieving real-time rendering on mobile devices
Solution Approach 2:
The patent changes the rendering approach from volumetric integration to neural network prediction by transforming the problem into a parameter estimation task where the neural network learns to map ray parameters directly to pixel colors, significantly reducing computation time
2Adaptability or versatility
If NeRF is deployed on mobile devices, then novel view synthesis is achieved, but high-end GPU and extra storage memory are required
Solution Approach 1:
The patent uses a lightweight neural network architecture designed specifically for mobile devices, replacing the need for expensive high-end GPUs and additional storage with a compact model that can run on standard mobile hardware
Solution Approach 2:
The patent segments the rendering process into two stages: a lightweight neural network for real-time prediction on mobile devices, and a separate high-quality rendering pipeline that can be activated only when needed, allowing the system to adapt to different hardware capabilities
3Speed
If NeLF is used to speed up rendering, then inference time is reduced, but computation intensity increases
Solution Approach 1:
The patent applies different computational strategies to different parts of the rendering process: using a lightweight neural network for most regions and only applying intensive computation where necessary, optimizing the balance between speed and computation intensity for mobile devices
Data Source
AI summary
A neural light field (NeLF) that runs real-time on mobile devices for neural rendering of three dimensional (3D) scenes, referred to as MobileR2L. The MobileR2L architecture runs efficiently on mobile devices with low latency and small size, and it achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world 3D scenes on mobile devices. The MobileR2L has a network backbone including a convolutional layer embedding an input image at a resolution, residual blocks uploading the embedded image, and super-resolution modules receiving the uploaded embedded image and rendering an output image having a higher resolution than the embedded image. The convolution layer generates a number of rays equal to a number of pixels in the input image, where a partial number of the rays is uploaded to the super-resolution modules.