Neural Volume Rendering With Surface-Concentrated Parameter Distribution
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Solution Overview
Problem
Existing 3D rendering techniques struggle to efficiently transform volume-rendering parameters to achieve desired features, particularly in focusing the distribution of parameters near the surface of a scene, leading to increased computational complexity and inefficiency.
Innovation Solution
A neural network model is trained to minimize a loss function that includes a regularization term based on the distribution of volume-rendering parameters, clustering them on the surface of a scene, using entropy measures and information potential to optimize the parameter distribution for efficient volume rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional volume rendering methods are used, then rendering completeness is maintained, but computational complexity increases and rendering efficiency decreases
Solution Approach 1:
The patent transforms volume rendering parameters through a neural network to concentrate the parameter distribution on the scene surface. This parameter transformation changes the sampling strategy from uniform volume sampling to surface-concentrated sampling, reducing the number of operations required while maintaining rendering quality.
Solution Approach 2:
The patent replaces traditional mechanical volume rendering algorithms with a neural network-based parameter transformation system. The neural network learns to map volume rendering parameters to a concentrated distribution, substituting complex iterative rendering mechanics with a learned transformation that achieves the same visual result with fewer operations.
2Loss of time
If volume rendering parameters are uniformly distributed, then complete scene coverage is achieved, but rendering time increases
Solution Approach 1:
The patent applies local quality by concentrating volume rendering parameters specifically on the scene surface region rather than uniformly across the entire volume. This localized parameter concentration achieves accurate surface rendering while reducing unnecessary computations in empty or less important regions, thereby reducing rendering time without sacrificing surface rendering precision.
Solution Approach 2:
The neural network performs preliminary transformation of volume rendering parameters before the actual rendering process, pre-concentrating them on the scene surface. This preliminary parameter transformation prepares the data in an optimal state for rendering, eliminating the need for time-consuming iterative adjustments during the rendering process itself.
3Productivity
If neural network transforms parameter distribution, then rendering efficiency improves, but training complexity increases
Solution Approach 1:
The neural network is trained to autonomously learn the optimal parameter transformation from volume-rendering parameters to surface-concentrated parameters. Through self-service learning on training data, the network automatically discovers the transformation pattern that concentrates parameters on the scene surface, eliminating the need for manual rule design and reducing long-term training complexity despite the initial investment in training infrastructure.
Data Source
AI summary
Disclosed are a method and device for representing rendered scenes. A data processing method of training a neural network model includes obtaining spatial information of sampling data, obtaining one or more volume-rendering parameters by inputting the spatial information of the sampling data to the neural network model, obtaining a regularization term based on a distribution of the volume-rendering parameters, performing volume rendering based on the volume-rendering parameters, and training the neural network model to minimize a loss function determined based on the regularization term and based on a difference between a ground truth image and an image that is estimated according to the volume rendering.