Automated 3D Mesh Merging via Clipping and Texture Blending
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Solution Overview
Problem
Current methods for merging multiple 3D textured meshes into a large and complex 3D model are labor-intensive and require manual alignment and editing, lacking an efficient automated approach for accurate reconstruction.
Innovation Solution
The method involves spatial alignment of meshes using iterative closest point algorithms, mesh clipping to remove redundant vertices, geometry refinement to close holes, and texture blending to create a seamless merged mesh, with false matching pairs identified and removed based on normal consistent connected groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual alignment and editing methods are used to merge 3D meshes, then accuracy of mesh merging can be maintained, but labor intensity and time consumption increase significantly
Solution Approach 1:
The system performs automatic spatial alignment, mesh clipping, geometry refinement, and texture blending without requiring manual intervention. The algorithm autonomously identifies overlapping regions, establishes matching pairs between meshes, and executes the merging process, eliminating the need for manual alignment and editing while maintaining accuracy
Solution Approach 2:
The patent replaces manual mechanical operations (hand alignment, manual editing, physical combination of meshes) with automated computational algorithms including iterative closest point algorithms for alignment, automatic clipping operations, and programmatic geometry refinement, thereby reducing labor intensity and time consumption
2Productivity
If automated merging algorithms are used to reduce manual labor, then productivity increases, but merging precision and quality may deteriorate
Solution Approach 1:
The system incorporates multiple refinement steps with quality control mechanisms. The geometry refinement process iteratively closes mesh concatenation holes, and the false matching pair removal uses feedback from normal consistent connected group analysis to eliminate incorrect correspondences, ensuring high merging quality while maintaining automated efficiency
Solution Approach 2:
The patent performs preliminary spatial alignment using iterative closest point algorithms before merging, and pre-identifies false matching pairs by analyzing normal consistent connected groups. These preliminary actions ensure that the subsequent automated merging process produces high-quality results by establishing correct spatial relationships and filtering out erroneous correspondences in advance
3Speed
If all matching pairs between meshes are accepted without verification, then processing speed increases, but false matches reduce merging accuracy
Solution Approach 1:
The system performs a selective verification process rather than accepting all matches or verifying every pair exhaustively. It estimates normal consistent connected groups for matching pairs and removes only those exceeding a threshold ratio, achieving a balance between processing speed and accuracy by applying verification selectively to potential false matches
Data Source
AI summary
A method of merging 3D meshes includes receiving a first mesh and a second mesh; performing spatial alignment to register the first mesh and the second mesh in a common world coordinate system; performing mesh clipping on the first mesh and the second mesh to remove redundant mesh vertices; performing geometry refinement around a clipping seam to close up mesh concatenation holes created by mesh clipping; and performing texture blending in regions adjacent the clipping seam to obtain a merged mesh.