3D Leaf Semantic Reconstruction Through 2D Mesh Parameterization
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Solution Overview
Problem
Existing methods for 3D semantic reconstruction of crop leaves are inefficient, labor-intensive, and accuracy is heavily dependent on manual operations, with reconstructed meshes lacking sufficient semantic information.
Innovation Solution
A method involving scale-space surface reconstruction, isotropic remeshing, ARAP mesh parameterization, and 2D semantic interpolation to generate a 3D semantic mesh model of crop leaves, incorporating semantic information through edge point determination and corresponding point connections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D digitization and mesh deformation are used to obtain 3D mesh models with semantic information, then semantic information can be obtained, but the workload is high and efficiency is low
Solution Approach 1:
The method segments the reconstruction process into distinct stages: surface reconstruction from point cloud, mesh parameterization to 2D domain, semantic segmentation on 2D mesh, and mapping back to 3D. This segmentation allows automated processing at each stage while maintaining semantic accuracy, resolving the contradiction between precision and efficiency.
Solution Approach 2:
The patent introduces a 2D parameterized domain as an intermediary between 3D point cloud and 3D mesh model. Semantic segmentation is performed on the 2D mesh, which is computationally easier and more accurate, then the results are mapped back to 3D space. This intermediary approach significantly improves efficiency while maintaining accuracy.
2Measurement precision
If manual operations are used in 3D semantic modeling, then semantic information can be obtained, but accuracy is greatly affected by manual operations
Solution Approach 1:
The system performs self-service by automatically conducting surface reconstruction, mesh parameterization, semantic segmentation, and result mapping without manual intervention. The automated pipeline eliminates the subjectivity and variability inherent in manual operations, improving both accuracy and reducing operational complexity.
Solution Approach 2:
The patent replaces manual mechanical operations with computational algorithms. Surface reconstruction algorithms, mesh parameterization methods, and automated semantic segmentation replace hand-crafted mesh deformation, eliminating manual complexity while maintaining or improving accuracy through consistent automated processing.
3Loss of information
If extensive manual operations are performed for 3D semantic reconstruction, then semantic information can be obtained, but reconstruction efficiency is low
Solution Approach 1:
The method performs preliminary surface reconstruction and mesh parameterization to create a 2D parameterized domain before semantic segmentation. This preliminary preparation enables efficient automated semantic labeling, which is then mapped back to 3D, significantly reducing total reconstruction time while preserving complete semantic information.
Solution Approach 2:
The patent transforms the 3D mesh into a 2D parameterized domain for semantic segmentation, then maps results back to 3D. This dimensionality change exploits the computational advantages of 2D processing while maintaining 3D semantic completeness, dramatically improving reconstruction efficiency without losing semantic information.
Data Source
AI summary
This application relates to the technical field of image processing, and provides a three-dimensional (3D) semantic reconstruction method and apparatus of a leaf, an electronic device, and a storage medium. The 3D semantic reconstruction method of a leaf includes: taking 3D point cloud data of a crop leaf as an input, generating an initial mesh of the crop leaf based on the 3D point cloud data, remeshing the initial mesh, mapping a 3D mesh to a two-dimensional (2D) space by parameterization, determining semantic surface feature points of a 2D mesh, and performing 3D semantic reconstruction based on corresponding points of the semantic surface feature points in the 3D mesh to obtain a 3D semantic mesh model of the crop leaf.