AR View Rendering With Sparse NeRF Features for Real-Time Scenes
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Solution Overview
Problem
Neural radiance field (NeRF) view synthesis for real-time photorealistic view rendering on augmented reality (AR) devices faces challenges due to high computational and memory requirements, making it difficult to achieve efficient rendering of fine geometric details and view-dependent appearances.
Innovation Solution
Utilizing position-dependent and direction-dependent multilayer perceptrons (MLPs) to create sparse feature vectors, which are stored in a data structure for efficient rendering on AR devices, reducing computational and memory demands through precomputation and caching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If NeRF view synthesis is used to generate photorealistic images from novel viewpoints, then rendering quality and geometric detail are improved, but computational requirements and memory usage increase significantly
Solution Approach 1:
The patent pre-computes and stores feature vectors for camera rays passing through keyframes before rendering. This preliminary action allows the system to avoid performing full NeRF computations during real-time rendering, significantly reducing computational requirements while maintaining photorealistic quality. The feature vectors are cached in a data structure for efficient retrieval during rendering operations.
Solution Approach 2:
The patent segments the scene representation into sparse feature vectors stored in a data structure, rather than using the full continuous NeRF model during rendering. This segmentation allows the system to work with discrete, pre-computed features instead of performing intensive continuous optimization during real-time rendering, reducing computational load while preserving rendering quality.
2Manufacturing precision
If NeRF view synthesis is used to capture fine geometric details and view-dependent appearances, then scene representation accuracy is improved, but memory requirements increase
Solution Approach 1:
The patent extracts only the essential feature vectors from the full NeRF computation and stores them in a sparse data structure. Instead of retaining the complete continuous volumetric representation in memory, the system extracts and caches discrete feature vectors that can be efficiently stored and retrieved, reducing memory requirements while preserving the ability to render fine geometric details and view-dependent appearances.
3Speed
If real-time rendering is implemented on AR devices, then rendering speed is improved, but computational complexity increases
Solution Approach 1:
The patent performs complex NeRF computations and feature vector generation in advance, before the AR device needs to render. This preliminary computation transfers the computational complexity burden to an offline preprocessing stage, allowing the AR device to perform only simple feature vector retrieval and composition during real-time rendering, achieving both real-time speed and handling of complex scenes.
Data Source
AI summary
A method includes obtaining images of a scene and corresponding position data of a device that captures the images. The method also includes determining position data and direction data associated with camera rays passing through keyframes of the images. The method further includes using a position-dependent multilayer perceptron (MLP) and a direction-dependent MLP to create sparse feature vectors. The method also includes storing the sparse feature vectors in at least one data structure. The method further includes receiving a request to render the scene on an augmented reality (AR) device associated with a viewing direction. In addition, the method includes rendering the scene associated with the viewing direction using the sparse feature vectors in the at least one data structure.