Hybrid Differentiable Rendering for 3D Model Disentanglement
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Solution Overview
Problem
Existing rendering technologies face challenges in efficiently rendering 3D models while supporting advanced lighting effects, and in disentangling material from lighting in inverse graphics applications.
Innovation Solution
The proposed solution involves hybrid differentiable rendering, which combines aspects of rasterization and ray-tracing to efficiently render 3D models and support advanced lighting effects. This approach generates a differentiable mapping between pixels and 3D model portions, allowing for the disentanglement of material and lighting information using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If physics-based rendering methods are used to achieve photorealistic images with advanced lighting effects, then rendering quality is improved, but computational performance deteriorates and optimization complexity increases
Solution Approach 1:
The rendering system segments the rendering pipeline into distinct modules: a rasterization-based geometry processing stage for efficient pixel mapping, and a ray-tracing-based light transport stage for photorealistic lighting effects. This segmentation allows each component to optimize for its specific function, achieving both speed and quality.
Solution Approach 2:
The patent merges rasterization and ray-tracing into a unified hybrid rendering system. The rasterization component handles geometry projection and pixel coverage efficiently, while the ray-tracing component handles light transport and lighting effects. By combining these complementary approaches, the system achieves both computational efficiency and photorealistic quality.
2Manufacturing precision
If ray-tracing-based physics methods are used to support advanced lighting effects, then lighting accuracy is improved, but system complexity and difficulty of optimization increase
Solution Approach 1:
The light transport simulation is segmented from the geometry processing. The system first performs rasterization to establish pixel-geometry mappings, then separately performs ray-tracing for light transport. This segmentation reduces the complexity of optimizing the entire system by allowing independent optimization of each stage.
Solution Approach 2:
The patent introduces an intermediary representation layer that connects rasterization and ray-tracing. The rasterization stage produces intermediate results (pixel coverage, geometry mappings) that serve as input to the ray-tracing stage. This intermediary layer simplifies the overall system architecture by providing clear interfaces between components.
3Productivity
If conventional inverse graphics methods are used to train neural networks with synthetic data, then training efficiency is improved, but generalization to complex real-world images deteriorates
Solution Approach 1:
The system uses differentiable rendering to create synthetic images that copy the mathematical relationships of real-world optics. By training neural networks on these synthetic copies with known ground truth, the models learn to generalize to real-world images without requiring actual physical capture data, thus improving both training efficiency and generalization capability.
Solution Approach 2:
The patent changes the parameters of the rendering system to be differentiable with respect to material properties, lighting conditions, and camera parameters. This allows the rendering pipeline to serve as a differentiable forward model, enabling efficient training of inverse graphics models by computing gradients through the entire rendering pipeline.
4Quantity of substance
If self-supervised learning methods are used to recover scene intrinsics, then data requirements are reduced, but reliance on specific priors and difficult-to-capture data increases
Solution Approach 1:
Instead of relying on complex real-world capture settings, the system creates synthetic copies of scene intrinsics through differentiable rendering. The rendering pipeline generates synthetic images with known ground truth material properties, lighting, and geometry, providing supervision data that eliminates the need for complex capture settings while reducing data requirements.
Solution Approach 2:
The system performs preliminary computation of scene intrinsics through differentiable rendering before the actual inverse graphics task. By pre-computing the rendering equations and their derivatives, the system prepares training data that simplifies the inverse problem, reducing both data requirements and the complexity of capture settings needed.
Data Source
AI summary
In various examples, information may be received for a 3D model, such as 3D geometry information, lighting information, and material information. A machine learning model may be trained to disentangle the 3D geometry information, the lighting information, and/or material information from input data to provide the information, which may be used to project geometry of the 3D model onto an image plane to generate a mapping between pixels and portions of the 3D model. Rasterization may then use the mapping to determine which pixels are covered and in what manner, by the geometry. The mapping may also be used to compute radiance for points corresponding to the one or more 3D models using light transport simulation. Disclosed approaches may be used in various applications, such as image editing, 3D model editing, synthetic data generation, and/or data set augmentation.