Virtual Walkthroughs Using Neural Radiance Fields for Depth Accuracy
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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 images to generate view synthesis renderings, which are then processed to create virtual walkthrough videos that can be navigated in multiple directions, incorporating three-hundred and sixty degree views and seamless transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If neural radiance field models are used to generate view synthesis renderings, then photorealism and depth accuracy are improved, but computational cost and training time increase
Solution Approach 1:
The system performs preliminary action by training the neural radiance field model in advance to generate a virtual walkthrough video that is stored and can be replayed multiple times. This preliminary training phase captures the entire environment and creates a comprehensive virtual representation, so that subsequent queries and interactions can be served from this pre-generated content without requiring additional training time.
Solution Approach 2:
The system creates a copy of the physical environment in the form of a virtual walkthrough video generated by the neural radiance field model. This virtual copy contains photorealistic and depth-accurate representations of the environment, allowing users to interact with and explore the copied environment without needing to access the physical location, thereby reducing repeated computational overhead.
2Adaptability or versatility
If extensive user inputs are required for rendering engine interfaces, then customization and control are improved, but ease of operation and scalability worsen
Solution Approach 1:
The system implements self-service by automatically generating the virtual walkthrough video through the trained neural radiance field model without requiring extensive manual user inputs. The model autonomously processes the environment data, generates view synthesis renderings from multiple positions, and creates the complete virtual walkthrough experience, eliminating the need for skilled users to perform tedious modeling and rendering operations.
Solution Approach 2:
The system changes parameters by transitioning from traditional rendering engine approaches that require manual asset creation to a neural radiance field approach where the environment is represented as a continuous function. This parameter change allows the system to generate photorealistic views from any position and direction by simply querying the neural field, providing extensive customization without manual intervention.
3Ease of manufacture
If traditional rendering engines are used, then asset control and scene generation are improved, but photorealism and depth accuracy worsen
Solution Approach 1:
The system replaces the mechanical system of traditional rendering engines that rely on manual asset creation and scene construction with a neural radiance field model. This substitution enables the generation of photorealistic and depth-accurate environments by learning from input images and generating views through neural network inference, rather than through manual modeling and traditional rendering pipelines.
Solution Approach 2:
The system changes the fundamental parameters of scene representation from discrete graphical assets and materials to a continuous neural field that encodes radiance and depth information. This parameter change allows the system to generate photorealistic images with accurate depth information by querying the neural field at different positions and directions, achieving both ease of scene generation and high manufacturing precision.
4Ease of operation
If virtual walkthrough videos are generated and stored, then interactivity and navigation are improved, but storage requirements and processing overhead increase
Solution Approach 1:
The system creates a virtual copy of the environment in video format that can be stored and replayed. This copied representation provides full interactivity and navigation capabilities without requiring the original physical environment or complex real-time rendering systems. The video copy contains all necessary visual information for exploration, reducing storage requirements compared to storing multiple high-resolution images or 3D models.
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.