NeRF Graphics Rendering with a GPU-NPU Pipeline for API Compatibility
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Solution Overview
Problem
Neural radiance field (NeRF) methods are not compatible with public graphics application program interfaces (APIs) and require extensive training time before generating new images from new viewpoints, limiting their use in graphics rendering.
Innovation Solution
A GPU+NPU pipeline is implemented, where the GPU performs initial rendering and training of a NeRF model using 2D images and scene information, transitioning to NPU inference for faster image generation once the model is trained, with compatibility maintained through a graphics API.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If NeRF is used for graphics rendering, then photorealistic image generation is achieved, but compatibility with public graphics APIs is lost
Solution Approach 1:
A translation layer or adapter is introduced between the NeRF model and the graphics API to enable compatibility. The adapter translates API calls into operations that the NeRF model can process, allowing photorealistic rendering while maintaining standard graphics programming interfaces.
Solution Approach 2:
The rendering system is divided into separate modules: a NeRF-based implicit rendering engine and a traditional graphics API layer. This segmentation allows each component to operate independently with its strengths preserved - the NeRF model handles photorealistic generation while the API layer maintains compatibility.
2Manufacturing precision
If NeRF training is performed to generate new images from new viewpoints, then image quality is improved, but training time is excessive
Solution Approach 1:
The NeRF model is trained in advance on a comprehensive dataset of the scene from multiple viewpoints. This preliminary training creates a pre-processed implicit representation that can then rapidly generate new images without requiring additional training time during runtime.
Solution Approach 2:
The training process uses more training data and more viewpoints than the minimum required, creating an over-trained model that generalizes better and requires less computation during inference, effectively trading offline training time for online rendering speed.
Data Source
AI summary
There is provided a method and apparatus for graphics rendering using a neural processing unit. The method includes receiving, by the NPU, a plurality of images indicative of a 3D scene and scene information indicative of 3D scene and receiving, by the NPU from a graphics processing unit (GPU), intermediate information indicative of a rendering process performed by the GPU and associated with the 3D scene. The method further includes generating, by the NPU, a model of the 3D scene based at least in part on the plurality of images, the scene information and the intermediate information. The method additionally includes inferring, by the NPU, a new image indicative of the 3D scene based on the model of the 3D scene.