Mobile Neural Light Field Rendering With Partial-Ray Upsampling
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Solution Overview
Problem
Neural Rendering Fields (NeRF) are slow and require high-end GPUs for inference, 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 backbone and super-resolution modules to achieve real-time neural rendering on mobile devices, reducing memory and latency by forwarding a partial number of rays and upsampling to high resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing 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 creates a lightweight copy of the NeRF model called MobileR2L that replicates the core rendering functionality while using a simplified neural network architecture and reduced computational complexity, enabling real-time inference on mobile devices while maintaining acceptable rendering quality
Solution Approach 2:
The patent changes the computational parameters by using a shallower neural network with fewer parameters compared to the original NeRF, and by implementing a two-stage rendering process that processes only a subset of rays in the first stage and refines them in the second stage, thereby reducing computation time while preserving rendering quality
2Adaptability or versatility
If NeRF is deployed on mobile devices, then rendering capability is added, but hardware requirements become too high
Solution Approach 1:
The patent replaces the expensive high-end GPU hardware requirement with a mobile-friendly architecture that runs on standard mobile processors, using efficient convolutional neural networks and optimized memory access patterns that are compatible with mobile device hardware constraints
Solution Approach 2:
The patent segments the rendering process into two stages: a first stage that processes a subset of rays to generate initial renderings, and a second stage that refines the results. This segmentation allows the system to achieve high rendering quality while distributing computational load over time, making it feasible for mobile devices
3Productivity
If NeLF is used to speed up inference, then rendering speed is improved, but computational intensity increases
Solution Approach 1:
The patent applies partial action by processing only a subset of rays in the first rendering stage rather than all rays simultaneously. This allows the system to achieve real-time rendering speed by completing the most critical rendering tasks efficiently, while using a second stage to refine the results, thereby reducing overall computational intensity while maintaining rendering quality
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.