3D Model Generation from 2D Images via Gradient Descent
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Solution Overview
Problem
Conventional methods for generating three-dimensional (3D) object models are expensive and time-consuming, requiring manual drawing or expensive equipment like depth sensors or laser scanners.
Innovation Solution
A model generation system that uses two-dimensional images to create 3D object models by applying an iterative gradient descent process with a differentiable error function and constraints on model parameters, allowing for efficient generation of high-quality 3D models by reusing part models and reducing the number of free variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional techniques (manual drawing, depth sensors, laser scanners) are used to generate 3D models, then manufacturing precision and reliability are improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses 2D images as copies or projections of the 3D object to reconstruct the full three-dimensional model. Instead of requiring complex 3D scanning equipment, the system captures simplified 2D representations and uses computational algorithms (including neural networks and gradient descent optimization) to infer the complete 3D structure from these 2D copies, thereby reducing device complexity while maintaining model accuracy
Solution Approach 2:
The patent replaces mechanical/optical scanning systems (depth sensors, laser scanners) with a computational approach using 2D image processing and mathematical optimization. The mechanical system of physical scanning is substituted with an information-processing system that uses gradient descent algorithms and differentiable rendering to solve for 3D parameters from 2D observations
2Manufacturing precision
If conventional techniques (manual drawing, depth sensors, laser scanners) are used to generate 3D models, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining parameter spaces, constraint relationships, and optimization algorithms before the actual 3D reconstruction process. The system prepares the computational framework including differentiable rendering pipelines and gradient descent configurations in advance, allowing the actual model generation from 2D images to proceed more efficiently without requiring time-consuming manual operations or sequential scanning procedures
Solution Approach 2:
The patent replaces time-consuming manual drawing and physical scanning processes with automated computational methods. The system uses neural networks and gradient-based optimization to automatically infer 3D structures from 2D images, eliminating the need for slow manual modeling operations and reducing the time required for data capture and processing compared to conventional scanning techniques
3Manufacturing precision
If the number of free variables in model parameters is increased to represent complex objects, then manufacturing precision is improved, but device complexity and computational complexity increase
Solution Approach 1:
The patent transforms the parameter representation by introducing constraints that reduce the number of free variables. Instead of treating all 3D model parameters as independent variables, the system defines parameter spaces with explicit constraints (e.g., relating object dimensions, proportions, and geometric relationships) that reduce dimensionality while preserving the ability to represent complex shapes accurately
Solution Approach 2:
The patent creates a universal parameter framework where a reduced set of constrained parameters can represent multiple different objects and configurations. The constraint-based parameter space serves multiple functions: it reduces computational complexity, ensures geometric consistency, and allows the same parameter structure to model various object types, thereby achieving universality without sacrificing representational power
Data Source
AI summary
A model generation system generates three-dimensional object models based on two-dimensional images of an object. The model generation system can apply an iterative gradient decent process to model parameters for part models within an object model to compute a final set of model parameter values to generate the object model. To compute the final set of model parameter values, the model generation generates a reference image of the object model and compares the reference image to a received image. The model generation system uses a differentiable error function to score the reference image based on a received image. The model generation system updates the set of model parameter values based on the score for the reference image, and iteratively repeats the process until a reference image is sufficiently similar to the received image.