Neural Texturing for Real-Time Novel-View Image Rendering
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Solution Overview
Problem
Existing methods for novel-view image synthesis of transmissive jade models struggle with achieving real-time and ground-truth rendering, often compromising on realism for real-time performance or requiring offline rendering that disrupts real-time pipelines.
Innovation Solution
A method and system utilizing neural texturing and a synthesis rendering model with an NT input module, NT learning network module, and differentiable renderer to perform convolution, activation, and neural rendering, enabling real-time and ground-truth image synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If offline rendering is performed to obtain ground-truth rendering results, then rendering quality and realism are improved, but real-time performance deteriorates and the rendering pipeline becomes complex
Solution Approach 1:
The patent creates a neural network model that learns from ground-truth rendering results and generates synthesized images that replicate the quality of offline rendering. The neural network is trained on pairs of input images and their corresponding ground-truth renders, enabling it to produce high-quality outputs without requiring time-consuming offline rendering processes for each new input.
Solution Approach 2:
The patent replaces the traditional mechanical rendering pipeline (which involves complex ray tracing and material simulation) with a neural network-based synthesis system. The neural network learns the mapping from input images to rendered outputs and uses this learned representation to generate images quickly, substituting the computationally intensive physical rendering process with a data-driven approach.
2Productivity
If simplified computational methods are used to simulate materials, then real-time performance is improved, but rendering realism and accuracy deteriorate
Solution Approach 1:
The patent employs feedback mechanisms where the neural network continuously refines its output by comparing synthesized images against ground-truth renders during training. This feedback loop enables the model to learn and correct rendering artifacts, gradually improving realism while maintaining real-time inference capability. The feedback from training data allows the network to capture complex material properties without requiring complex real-time computation.
Solution Approach 2:
The patent changes the fundamental parameters of the rendering system by transitioning from physical rendering parameters (ray traces, material coefficients) to neural network parameters (weights, activations). This parameter transformation allows the system to achieve both speed and quality by learning optimal representations from training data rather than computing physical interactions in real-time.
3Manufacturing precision
If neural texturing and multi-layer skip connections are applied, then image synthesis quality and noise reduction are improved, but computational complexity increases
Solution Approach 1:
The patent divides the rendering task into multiple stages using multi-layer skip connections, where different layers of the neural network handle different aspects of image synthesis. The skip connections allow information to flow between layers, enabling the system to process complex material properties and lighting interactions in manageable segments rather than as a single monolithic computation.
Solution Approach 2:
The patent implements a nested neural network architecture where multiple processing layers are contained within each other, with inner layers processing detailed features and outer layers handling broader context. This nested structure allows the model to progressively refine image quality while managing computational complexity through hierarchical organization of processing tasks.
Data Source
AI summary
Provided are a method and system for novel-view image synthesis and rendering, a device and a medium. The method includes: acquiring initial information of a target model; performing neural texturing on the initial information to obtain neural texture (NT) information; and inputting the NT information to a synthesis rendering model to obtain a rendered image, where the synthesis rendering model includes an NT input module, an NT learning network module, and a differentiable renderer that are connected to each other; the NT input module receives the NT information, and transmits the NT information to the NT learning network module; the NT learning network module performs convolution and activation as well as concatenation on the NT information to obtain NT processed information; and the differentiable renderer adjusts and renders the NT processed information in a neural rendering manner to obtain the rendered image.