Neural Rendering Pipeline for Realistic 3D Image Quality
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Solution Overview
Problem
Conventional methods for generating photorealistic 3D content images are costly, time-consuming, and face limitations in realism due to the 'uncanny valley' effect, with separate research focuses on quality and realism often leading to trade-offs in resolution and noise, and there is a scarcity of training data for 3D graphics rendered images.
Innovation Solution
A neural network training method that utilizes real and rendered images to generate high-quality, realistic images by degrading, restoring, and generating images using deep learning structures, with error analysis to improve quality and realism, including generative adversarial networks and identity preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional photorealistic rendering methods are used, then image quality and realism are improved, but cost and time consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks using paired datasets of low-quality and high-quality images before actual rendering. This pre-training phase prepares the network to automatically enhance images, eliminating the need for time-consuming manual adjustments and iterative rendering during production, thus resolving the contradiction between image quality and time consumption.
Solution Approach 2:
The patent replaces the mechanical rendering system (traditional ray tracing and physics-based simulation) with a neural network-based system. Instead of performing complex computational operations through hardware and software rendering pipelines, the system uses learned patterns from training data to generate high-quality images, significantly reducing time consumption while maintaining or improving image quality.
2Manufacturing precision
If manual creation of high-quality 3D object models is performed, then image quality is improved, but cost and time increase
Solution Approach 1:
The patent uses copying by training the neural network on pairs of low-quality and high-quality reference images. The network learns to copy the enhancement patterns from the high-quality images and apply them to new low-quality inputs, eliminating the need for manual creation of high-quality 3D models for each case and significantly reducing costs while maintaining image quality.
Solution Approach 2:
The patent applies parameter changes by transforming images through the neural network, which adjusts multiple parameters simultaneously (resolution, noise levels, color accuracy, lighting) based on learned patterns. This automated parameter adjustment replaces manual model creation, reducing both cost and time while achieving consistent high-quality results.
3Manufacturing precision
If deep neural network technology is used for quality improvement, then image quality is improved, but training data for 3D graphics rendered images is scarce
Solution Approach 1:
The patent uses an intermediary approach by introducing a synthetic data generation step. Since real paired datasets of low-quality and high-quality 3D rendered images are scarce, the system generates synthetic training pairs using available 3D models and controlled rendering conditions. This intermediary synthetic data serves as a bridge, enabling effective neural network training without requiring large quantities of real-world paired datasets.
4Reliability
If focus is placed on improving realism, then realism is improved, but resolution decreases and noise increases
Solution Approach 1:
The patent merges multiple objectives by designing a loss function that combines realism assessment with quality metrics (resolution, noise, sharpness). Instead of optimizing for realism alone, the system simultaneously optimizes for both realism and image quality parameters, producing images that achieve photorealism without sacrificing resolution or introducing excessive noise.
Data Source
AI summary
Disclosed herein is a method for improving the quality and realism of a rendered image. The method includes receiving training data including a real image and a rendered image, generating a low-quality image using the training data, generating a high-quality image using the low-quality image, generating a realistic image using the high-quality image, and training a neural network using an error calculated based on the high-quality image and the realistic image.