Quantized Neural Radiance Field Training for Real-Time Rendering
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Solution Overview
Problem
Neural Radiance Field (NeRF) frameworks for generating renderings are computationally intensive, limiting their application in real-time scenarios, and reducing sample points degrades rendering quality.
Innovation Solution
A method involving a two-phase training process and quantization framework for Neural Radiance Fields, focusing on collecting sample points near the object's surface and applying quantization to optimize data processing for AI chips, reducing computational burden while maintaining rendering quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the number of sample points is reduced to accelerate rendering speed, then rendering efficiency is improved, but rendering quality deteriorates
Solution Approach 1:
The training process is divided into two distinct phases: a first training process using a plurality of first test sample points, followed by a second training process using a plurality of second test sample points. This segmentation allows the system to first learn from diverse samples and then refine from focused samples, achieving both speed and quality.
Solution Approach 2:
The patent applies different sampling strategies to different stages of training. The first training process uses a broader sampling approach, while the second training process uses a more focused approach with second test sample points. This local differentiation optimizes both training efficiency and final rendering quality without requiring uniformly high sample counts throughout.
2Productivity
If quantization is applied to optimize data processing for AI chips, then computational efficiency is improved, but precision of neural network parameters is reduced
Solution Approach 1:
The patent applies quantization to transform the parameters of the machine learning model from high-precision floating-point representations to lower-precision integer representations. This parameter transformation enables efficient processing on AI chips while maintaining acceptable rendering quality through the two-phase training approach that compensates for quantization effects.
Data Source
AI summary
A computer-implemented method includes encoding a radiance field of an object onto a machine learning model; conducting, based on a set of training images of the object, a training process on the machine learning model to obtain a trained machine learning model, wherein the training process includes a first training process using a plurality of first test sample points followed by a second training process using a plurality of second test sample points located within a threshold distance from a surface region of the object; obtaining target view parameters indicating a view direction of the object; obtaining a plurality of rays associated with a target image of the object; obtaining render sample points on the plurality of rays associated with the target image; and rendering, by inputting the render sample points to the trained machine learning model, colors associated with the pixels of the target image.