Surface Reconstruction from Unstructured Point Sets
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Solution Overview
Problem
Existing surface reconstruction algorithms for heart chamber geometry are inefficient and unable to effectively model complex, non-convex surfaces from clouds of geometry points, with convex hull algorithms limited to convex boundaries and alpha shape methods being computationally intensive.
Innovation Solution
A method involving binning geometry points into a 3D array of voxels, applying an image close algorithm with dilation and erosion processes to create a segmented volume, and using a surface construction algorithm like marching cubes to form a surface model, allowing for efficient construction of complex, non-convex surfaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If convex hull algorithms are used to reconstruct surface from geometry points, then the surface construction is simple and fast, but the algorithm cannot estimate non-convex boundaries
Solution Approach 1:
The patent segments the surface reconstruction process into multiple stages: first creating a convex hull approximation, then identifying and processing non-convex regions separately through alpha shape calculation, and finally merging the results. This segmentation allows the algorithm to maintain computational efficiency while accurately modeling non-convex boundaries that would otherwise be impossible to represent.
Solution Approach 2:
The patent employs dynamic adjustment of the alpha parameter in alpha shape algorithms to adaptively capture different levels of surface detail. By dynamically adjusting this parameter and iteratively refining the surface model, the system can transition from a simple convex approximation to a detailed non-convex representation, achieving both speed and accuracy.
2Adaptability or versatility
If alpha shape algorithms are used to reconstruct surface from geometry points, then the algorithm can estimate non-convex boundaries, but the computation becomes intensive
Solution Approach 1:
The patent performs preliminary actions by first creating a convex hull approximation and pre-processing the geometry points to identify potential non-convex regions. This preliminary structuring reduces the complexity of subsequent alpha shape calculations, allowing the algorithm to focus computational resources only on critical non-convex areas rather than processing the entire point cloud uniformly.
Solution Approach 2:
The patent divides the surface reconstruction into segments: convex hull generation, non-convex region identification, and localized alpha shape calculation. By segmenting the computation this way, the algorithm avoids the intensive processing required for complete alpha shape calculation while still accurately representing non-convex features where needed.
3Manufacturing precision
If radial binning algorithms are used to create shell from geometry points, then the surface smoothing can be applied, but the method is complex and computationally demanding
Solution Approach 1:
The patent merges multiple surface construction approaches into a unified algorithm that combines convex hull generation, alpha shape calculation, and radial binning in a single integrated framework. This merging allows the system to leverage the strengths of each method while avoiding their individual weaknesses, achieving surface smoothness without requiring separate complex processing stages.
Solution Approach 2:
The patent creates a universal surface reconstruction algorithm that can handle various surface types (convex, non-convex, complex geometries) using a single multi-functional approach. The algorithm universally applies convex hull generation, non-convex region detection, and radial binning operations, eliminating the need for separate specialized algorithms for different geometric types and reducing overall system complexity.
Data Source
AI summary
A method of modeling a surface from a plurality of geometry points representing an object generally includes binning the plurality of geometry points into an n-dimensional array of cells and associating a binary value with each cell; applying a dilation algorithm to the binned plurality of geometry points to output a dilated binary representation of the plurality of geometry points; applying an erosion algorithm to the dilated binary representation of the plurality of geometry points to output a segmented volume; and applying a surface construction algorithm to the segmented volume to form a surface model of the object.