Learned Mesh Ray Tracing for Real-Time 3D Rendering
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Solution Overview
Problem
Existing 3D computer graphics processing techniques are computationally demanding and inefficient, particularly when using machine learning methods like NeRF, which struggle to render 3D scenes in real-time due to high computational requirements and suboptimal utilization of existing hardware.
Innovation Solution
Integrate a learned triangle mesh and feature texture with a graphics processing unit (GPU) for ray tracing, utilizing a bounding volume hierarchy (BVH) technique to enhance rendering efficiency, and employ a shader processor for view-dependent color synthesis and neural network updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning techniques like NeRF are used for 3D scene synthesis, then rendering quality and view-dependent color accuracy are improved, but computational complexity and rendering time increase significantly
Solution Approach 1:
The system segments the rendering process into distinct components: ray traversal for visibility determination, shader processing for color synthesis, and neural network processing for feature extraction. This segmentation allows each component to be optimized independently and executed in parallel on suitable hardware, reducing overall computational complexity while maintaining rendering quality.
Solution Approach 2:
The patent transitions from traditional volumetric rendering to a hybrid approach that incorporates triangle mesh geometry. By adding the geometric dimension (triangle meshes) to the neural radiance field, the system achieves faster ray marching while preserving view-dependent color accuracy, effectively solving the contradiction between quality and complexity.
2Speed
If traditional ray tracing with triangle meshes is used, then rendering speed is improved, but view-dependent color accuracy and visual realism deteriorate
Solution Approach 1:
The system merges traditional triangle mesh ray tracing with neural radiance field techniques. The triangle mesh provides fast geometric intersection and visibility determination, while the neural network processes ray attributes to synthesize view-dependent colors. This combination achieves both rendering speed and color accuracy by leveraging the strengths of both approaches.
Solution Approach 2:
The patent introduces an intermediary shader processor that bridges geometric rendering and neural rendering. The shader receives geometric data from the triangle mesh and neural features from the trained network, then synthesizes the final view-dependent colors. This intermediary component enables the system to achieve both speed and accuracy by coordinating multiple data sources.
3Measurement precision
If full neural network processing is applied to every ray, then rendering accuracy is improved, but hardware utilization efficiency deteriorates
Solution Approach 1:
Instead of applying full neural network processing to every ray, the system uses partial action by pre-training the neural network offline to learn scene features from input images. During rendering, only inference is performed on the trained network, which is computationally much lighter than full training. This partial application of neural processing maintains accuracy while dramatically improving hardware utilization efficiency.
Solution Approach 2:
The patent performs preliminary neural network training offline before rendering. The network learns scene characteristics, geometry, and material properties from input images in advance. This preliminary action transfers computational burden from the rendering phase to the training phase, allowing efficient real-time rendering with high hardware utilization while maintaining rendering accuracy.
Data Source
AI summary
Aspects of the disclosure are directed to three-dimensional (3D) computer graphics processing. In accordance with one aspect, the disclosure includes a memory configured to store a learned triangle mesh and a learned feature texture; a graphics processing unit (GPU) coupled to the memory, the GPU configured to render an inferred three-dimensional (3D) scene based on the learned triangle mesh and the learned feature texture using a ray tracing; and a display unit coupled to the GPU, the display unit configured to display the inferred 3D scene.