Neural Radiance Field Modeling for User Object View Synthesis
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Solution Overview
Problem
Three-dimensional modeling, object segmentation, and novel view rendering are inaccessible to users due to the limitations of existing techniques, which rely heavily on photography and large data sets, making it cumbersome to understand object size, compatibility, and environment without physically rearranging items.
Innovation Solution
A platform utilizing neural radiance field models to generate three-dimensional representations of user objects, enabling users to create virtual catalogs, rearrange environments, and compare objects through augmented reality, using user images to train models for view synthesis and rendering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional photography and large data sets are used for three-dimensional modeling, then measurement precision can be improved, but device complexity and ease of operation deteriorate
Solution Approach 1:
The patent replaces traditional mechanical 3D scanning and photography systems with a neural radiance field model that processes images through machine learning algorithms. This substitution eliminates complex hardware requirements while maintaining measurement precision through computational methods.
Solution Approach 2:
The system creates virtual copies of physical objects through neural rendering, generating photorealistic representations that capture three-dimensional characteristics without requiring physical measurements or complex imaging equipment. The neural radiance field model learns to synthesize novel views from limited input images.
2Ease of operation
If traditional photography is used for object representation, then ease of operation is improved, but loss of information increases
Solution Approach 1:
The patent transitions from two-dimensional photographs to three-dimensional virtual representations by training neural radiance field models on image data. This dimensional enhancement recovers depth, volume, and spatial relationships that are lost in flat images, while maintaining ease of operation through automated processing.
3Measurement precision
If physical rearrangement is used to understand object compatibility, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary three-dimensional modeling and virtual rendering before physical rearrangement is needed. By pre-processing images into neural radiance field models, the system enables rapid virtual prototyping and compatibility assessment, eliminating time-consuming physical trial-and-error while maintaining measurement accuracy.
Solution Approach 2:
The patent creates virtual copies of objects that can be manipulated and tested in digital environments. These neural rendered representations allow users to assess compatibility, scale, and spatial relationships without moving physical objects, significantly reducing time loss while preserving measurement precision.
4Manufacturing precision
If neural radiance field models are trained on user images, then manufacturing precision is improved, but use of energy increases
Solution Approach 1:
The system uses partial action by training neural radiance field models on a limited subset of training images rather than requiring exhaustive data collection. This approach achieves sufficient manufacturing precision for virtual representation while reducing the computational energy required compared to comprehensive 3D scanning or extensive photography.
Data Source
AI summary
Systems and methods for enabling users to generate and utilize neural radiance field models can include obtaining user image data and training one or more neural radiance field models based on the user image data. The systems and methods can include obtaining user images based on a determination that the user images depicted objects of a particular object type. The trained neural radiance field models can then be utilized for view synthesis image generation of the particular user objects.