3D Mesh Retopologization Using Automated Correspondence Matching
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Solution Overview
Problem
Manual retopologization of 3D meshes is time-consuming and prone to human errors and inconsistencies due to the need for manual selection of correspondence points, which hampers efficient and accurate reconstruction of 3D models.
Innovation Solution
Automated retopologization of 3D meshes through the automated selection and adjustment of correspondence points using a matching score based on surface normal similarity, distance, and surface curvature, followed by photometric refinement to improve the representation of visual details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual retopologization is performed with manual selection of correspondence points, then the process allows human judgment and adjustment, but it is time-consuming and prone to human errors and inconsistencies
Solution Approach 1:
The system performs automated retopologization by having the computer automatically select and adjust correspondence points using algorithms that compute matching scores based on surface normal similarity, distance, and surface curvature. This self-service approach eliminates manual human intervention while maintaining high accuracy through objective computational metrics, directly resolving the contradiction between reliability and time consumption.
Solution Approach 2:
The patent replaces the mechanical manual process of selecting correspondence points with an automated computational system. The system uses algorithms to calculate matching scores based on surface normals, distances, and curvatures, substituting human manual operations with automated mechanical/computational processes that are both faster and more consistent, thereby reducing time while maintaining or improving accuracy.
2Productivity
If automated retopologization is implemented, then time consumption is reduced and consistency is improved, but computational complexity increases
Solution Approach 1:
The automated retopologization process is divided into distinct sequential stages: (1) generating an input scan mesh from images, (2) generating an intermediate mesh with approximate geometry, (3) identifying keypoints on the intermediate mesh, (4) computing matching scores based on surface normals and distances, (5) selecting correspondence points, and (6) applying photometric refinement. This segmentation of the complex automated process into manageable stages improves productivity while controlling computational complexity by processing tasks in organized steps rather than as a monolithic complex operation.
Solution Approach 2:
The system performs preliminary actions by first generating an intermediate mesh that approximates the input scan mesh geometry before selecting correspondence points. This preliminary mesh generation simplifies subsequent processing by providing a structured framework, reducing the overall computational complexity while enabling faster automated retopologization. The preliminary action of creating the intermediate mesh prepares the data structure for efficient correspondence point selection.
3Measurement precision
If multiple metrics are used for matching score computation, then selection accuracy is improved, but computational cost increases
Solution Approach 1:
The matching score computation applies different quality metrics locally to different aspects of correspondence point selection: surface normal similarity measures local orientation match, distance measures local position match, and surface curvature measures local geometric characteristics. By applying multiple specialized metrics to different local properties rather than one global metric, the system achieves high selection accuracy while managing computational resources efficiently through targeted local measurements.
Data Source
AI summary
Methods and apparatuses for automating the retopologization of 3D meshes including the automated selection and adjustment of correspondence points are described. The automated selection of correspondence points may be performed to refine locations of correspondence points using a matching score that is computed based on surface normal similarity between surfaces corresponding with a candidate correspondence point on an input scan mesh and a point on a morphable model of 3D surfaces. The matching score may also take into account a distance between a candidate correspondence point on the input scan mesh and a corresponding point on the morphable model of 3D surfaces and similarities in surface features, such as similarities in surface curvature at the candidate correspondence point on the input scan mesh and the corresponding point on the morphable model of 3D surfaces.