NeRF Virtual Walkthrough Rendering for Interactive Depth-Aware Navigation
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Solution Overview
Problem
Existing systems fail to provide interactive and photorealistic virtual walkthroughs of environments, lacking interactivity and depth accuracy, and rely on tedious user inputs for rendering, limiting scalability.
Innovation Solution
Utilizing neural radiance field models trained on environment images to generate view synthesis renderings, which are then processed to create virtual walkthrough videos with multi-directional capabilities, enabling photorealistic and depth-aware navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If rendering engine interfaces are used to generate assets for rendering a scene, then the generated assets can be photorealistic, but extensive and tedious inputs by an experienced user are required and scaling is limited
Solution Approach 1:
The neural radiance field model automatically processes input images to generate the environment representation and view synthesis renderings without requiring manual asset creation or configuration by users. The system self-adjusts parameters and generates photorealistic renderings autonomously based on the trained model
Solution Approach 2:
The patent replaces the manual mechanical process of asset creation by experienced users with an automated neural radiance field model that generates photorealistic renderings through machine learning, eliminating the need for tedious user inputs while maintaining high quality output
2Manufacturing precision
If rendering engine interfaces are used to generate assets for rendering a scene, then the generated assets can be photorealistic, but the process is time consuming and scaling is limited
Solution Approach 1:
The neural radiance field model is trained in advance on a dataset of images from the environment, creating a pre-processed representation that enables rapid generation of view synthesis renderings. This preliminary training phase allows subsequent rendering operations to be performed quickly without repeating the full processing pipeline
Solution Approach 2:
The patent replaces the time-consuming manual asset generation process with an automated neural radiance field model that can generate photorealistic renderings rapidly. The model substitutes complex rendering computations with learned predictions, significantly improving generation speed and enabling scaling to multiple environments
3Device complexity
If traditional image search results are provided, then the system is simple, but the results lack interactivity and fail to capture the dimensionality of the location
Solution Approach 1:
The patent transitions from 2D static images to 3D immersive virtual walkthroughs by using neural radiance field models to generate view synthesis renderings from multiple positions and angles. This adds spatial dimensionality and interactivity, allowing users to navigate and explore environments virtually while maintaining computational efficiency through the trained model
Data Source
AI summary
Systems and methods for generating and providing a virtual walkthrough interface can include generating a virtual walkthrough video based on view synthesis renderings generated by neural radiance field model. The neural radiance field model can be trained based on a plurality of images of an environment and may generate the view synthesis renderings based on processing positions along a determined walkthrough path. The generated virtual walkthrough video can then be scrubbed through to provide the virtual walkthrough interface.