Heterogeneous 3D Fusion Rendering via Neural Radiance Field Segmentation
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Solution Overview
Problem
Current three-dimensional multi-object or scene fusion rendering technologies face challenges in achieving high authenticity and real-time operability, particularly due to the limitations of traditional rendering engines in adapting to advanced neural radiance field representations.
Innovation Solution
A heterogeneous three-dimensional multi-object fusion rendering method that combines neural radiance field reconstruction with traditional rendering engines like Unreal Engine, involving steps such as implicit representation, volume-based rendering, and multi-object scene synthesis to enhance authenticity and real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rendering engines are used for three-dimensional multi-object fusion rendering, then real-time operability is maintained, but authenticity and rendering fidelity are insufficient
Solution Approach 1:
The rendering system is segmented into two independent parts: a neural radiance field rendering module for high-fidelity rendering of specific objects, and a traditional rendering engine for real-time scene composition. Each module operates independently with its own optimization, allowing the neural rendering to achieve high authenticity without compromising overall real-time performance.
Solution Approach 2:
The patent merges neural radiance field rendering results with traditional rendering engine output through composite rendering. The high-fidelity neural rendering of key objects is integrated into the real-time rendered scene, combining the authenticity benefits of neural rendering with the real-time capabilities of traditional engines.
2Reliability
If neural radiance field representation is used for three-dimensional objects, then rendering fidelity is improved, but computing and storage resources are consumed
Solution Approach 1:
Neural radiance field rendering is applied selectively only to key objects that require high rendering fidelity, rather than to the entire scene. This localized application reduces the overall computing and storage resource consumption while maintaining high fidelity where most needed.
Solution Approach 2:
The system uses a hybrid approach where only critical objects receive the computationally expensive neural radiance field treatment, while other objects use traditional rendering methods. This partial application of neural rendering achieves high fidelity for important elements without the excessive resource consumption of full-scene neural rendering.
3Reliability
If neural radiance field reconstruction is performed on all three-dimensional objects, then rendering authenticity is improved, but device complexity increases
Solution Approach 1:
The system architecture is segmented to separate neural radiance field processing from traditional rendering. Only specific objects are processed through the complex neural network pipeline, while other objects follow traditional rendering paths, reducing overall system complexity.
Solution Approach 2:
A scene management system acts as an intermediary that intelligently routes objects to appropriate rendering pipelines. This mediator determines which objects require neural radiance field processing based on importance metrics, simplifying the overall system by providing a clear decision-making layer between object selection and rendering execution.
Data Source
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AI summary
The present disclosure discloses a heterogeneous three-dimensional multi-object fusion rendering method, device, and system, and belongs to the technical field of computer vision and graphics. The method comprises the following steps: S 1. neural radiance field reconstruction: performing implicit representation on a three-dimensional scene or object by using a neural radiance field to obtain an implicit representation model and a corresponding rough geometric model; S2. rendering of neural radiance field: importing the implicit representation model into a neural radiance field renderer for rendering to obtain a new view angle under a given camera pose; S3. rendering: importing the rough geometric model into a rendering engine, and generating a shadow of the geometric model in a rendered scene; and S4. multi-object scene synthesis: synthesizing a picture rendered in the rendering engine and a picture rendered by the neural radiance field. The present disclosure solves the problem of poor authenticity and real-time effects of current three-dimensional multi-object or scene fusion rendering, and promotes the implementation of neural rendering technology.