3D Reconstruction Using Smooth Maps for Silhouette Optimization
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Solution Overview
Problem
Current 3D reconstruction methods are inefficient and lack accuracy in reconstructing real objects from 2D images, particularly when dealing with objects that have complex silhouettes and varying viewpoints.
Innovation Solution
A computer-implemented method that uses smooth maps with pixel values representing contour presence, iteratively optimizing energy functions to reward projections of silhouette vertices with high contour presence, allowing for accurate and robust 3D modeling by combining structure-from-motion analysis with gradient-based optimization techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional 3D reconstruction methods are used, then the process can handle complex silhouettes and varying viewpoints, but the reconstruction accuracy and efficiency deteriorate
Solution Approach 1:
The patent transforms discrete silhouette matching into a continuous optimization problem by introducing smooth maps and energy functions. The discrete parameter of silhouette vertex projection is changed to continuous energy minimization, allowing gradient-based optimization methods to be applied, which significantly improves both accuracy and computational efficiency.
Solution Approach 2:
The patent replaces traditional mechanical/geometric matching approaches with a physics-inspired energy minimization framework. By defining an energy function that measures the alignment between 3D silhouette projections and 2D image contours, the system uses gradient descent optimization instead of iterative geometric adjustments, improving convergence speed and accuracy.
2Adaptability or versatility
If discrete silhouette matching is used, then the method can handle varying viewpoints, but the optimization landscape becomes non-smooth and convergence deteriorates
Solution Approach 1:
The patent applies smoothing operations (Gaussian blur, Laplace convolution) to transform the discrete contour probability map into a smooth continuous field. This parameter transformation maintains the adaptability to handle varying viewpoints while ensuring the energy landscape is smooth and differentiable, guaranteeing reliable convergence of gradient-based optimization methods.
Solution Approach 2:
The patent introduces smooth maps as an intermediary between the discrete 3D silhouette vertices and the continuous 2D image contours. These smooth maps act as a bridge that preserves the geometric relationship while providing a differentiable energy landscape, enabling reliable optimization convergence across varying viewpoints.
3Productivity
If simple contour maps are used, then the processing is computationally efficient, but the reconstruction precision for complex objects deteriorates
Solution Approach 1:
The patent enhances the contour representation by applying smoothing operations (Gaussian blur with various kernel sizes, Laplace convolution) to the contour probability map. This transforms simple binary contours into smooth continuous fields with rich gradient information, significantly improving reconstruction precision for complex objects while maintaining computational efficiency through analytical gradient computation.
Solution Approach 2:
The patent combines multiple processing techniques into a composite energy function: contour detection, Gaussian smoothing with multiple kernel sizes, Laplace convolution, and gradient-based optimization. This composite approach integrates the advantages of each method, achieving high reconstruction precision while maintaining computational efficiency through the unified energy minimization framework.
Data Source
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AI summary
The invention notably relates to a computer-implemented method for 3D reconstruction. The method comprises providing 2D images and, for each 2D image, camera parameters which define a perspective projection. The 2D images all represent a same real object. The real object is fixed. The method also comprises providing, for each 2D image, a smooth map. The smooth map has pixel values, and each pixel value represents a measurement of contour presence. The method also comprises determining a 3D modeled object that represents the real object. The determining iteratively optimizes an energy. The energy rewards, for each smooth map, projections of silhouette vertices of the 3D modeled object having pixel values representing a high measurement of contour presence. This forms an improved solution for 3D reconstruction.