Implemented the normal estimation functionality to the project. The primary focus was on improving the normal estimation process and making the code more efficient, readable, and extensible. Below is a summary of the key changes made:
Parallelized k-Nearest Neighbors Search using KDTree:
Optimized the k-nearest neighbors search using the KDTree data structure.
Employed the Rayon library for parallel processing, reducing computation time significantly.
Covariance Matrix Computation:
Implemented the computation of covariance matrices for accurate normal estimation.
Explained the need for covariance matrices in capturing local surface structure.
Eigenvalue-Eigenvector Computation:
Computed eigenvalues and eigenvectors from covariance matrices.
Clarified why the first eigenvector with the smallest eigenvalue represents the normal direction.
Specialized Normal Orientation Propagation:
Addressed challenges related to normal orientation consistency.
Designed a specialized Breadth-First Search (BFS) algorithm to propagate normal orientation.
Proposed Commit Message:
Implement Normal Estimation Enhancements
This commit introduces significant enhancements to the normal estimation process.
Parallelization, covariance matrix computation, eigenvalue-eigenvector computation,
and specialized normal orientation propagation have been added to improve accuracy
and efficiency.
Summary:
Normal Estimation Implementation
Type of change:
✨ Enhancement
Changes Made:
Implemented the normal estimation functionality to the project. The primary focus was on improving the normal estimation process and making the code more efficient, readable, and extensible. Below is a summary of the key changes made:
Parallelized k-Nearest Neighbors Search using KDTree:
Covariance Matrix Computation:
Eigenvalue-Eigenvector Computation:
Specialized Normal Orientation Propagation:
Proposed Commit Message: